EDBT 2026 Demo / reviewers in the wild / expert
Niko Sünderhauf
dblp:51/4711
· DBLP profile ↗
46ranked-venue papers
14as first author
14since 2021 · last 2025
0000-0001-5286-3789ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 39 · 13 first-author · 11 since 2021Systems, architecture and hardware · 30 · 12 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 11 · 7 since 2021Applied, interdisciplinary, general and emerging computing · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Multi-View Pose-Agnostic Change Localization with Zero LabelsabstractAutonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7× and 1.5× improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations. Our code and the dataset are available at MV-3DCD.github.io. Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald G. Dansereau, Niko Sünderhauf, Dimity Miller |
CVPR | 5 |
| 2025 | RMMI: Reactive Mobile Manipulation using an Implicit Neural MapabstractMobile manipulator robots operating in complex domestic and industrial environments must effectively coordinate their base and arm motions while avoiding obstacles. While current reactive control methods gracefully achieve this coordination, they rely on simplified and idealised geometric representations of the environment to avoid collisions. This limits their performance in cluttered environments. To address this problem, we introduce RMMI, a reactive control framework that leverages the ability of neural Signed Distance Fields (SDFs) to provide a continuous and differentiable representation of the environment’s geometry. RMMI formulates a quadratic program that optimises jointly for robot base and arm motion, maximises the manipulability, and avoids collisions through a set of inequality constraints. These constraints are constructed by querying the SDF for the distance and direction to the closest obstacle for a large number of sampling points on the robot. We evaluate RMMI both in simulation and in a set of real-world experiments. For reaching in cluttered environments, we observe a 25% increase in success rate. For additional details, code, and experiment videos, please visit https://rmmi.github.io/. Nicolas Marticorena, Tobias Fischer 0001, Jesse Haviland, Niko Sünderhauf |
IROS | 4 |
| 2024 | Open-Set Recognition in the Age of Vision-Language Models
Dimity Miller, Niko Sünderhauf, Alex Kenna, Keita Mason |
ECCV (42) | 2 |
| 2024 | RoboHop: Segment-based Topological Map Representation for Open-World Visual NavigationabstractMapping is crucial for spatial reasoning, planning and robot navigation. Existing approaches range from metric, which require precise geometry-based optimization, to purely topological, where image-as-node based graphs lack explicit object-level reasoning and interconnectivity. In this paper, we propose a novel topological representation of an environment based on , which are semantically meaningful and open-vocabulary queryable, conferring several advantages over previous works based on pixel-level features. Unlike 3D scene graphs, we create a purely topological graph with segments as nodes, where edges are formed by a) associating segment-level descriptors between pairs of consecutive images and b) connecting neighboring segments within an image using their pixel centroids. This unveils a continuous sense of a place, defined by inter-image persistence of segments along with their intra-image neighbours. It further enables us to represent and update segment-level descriptors through neighborhood aggregation using graph convolution layers, which improves robot localization based on segment-level retrieval. Using real-world data, we show how our proposed map representation can be used to i) generate navigation plans in the form of hops over segments and ii) search for target objects using natural language queries describing spatial relations of objects. Furthermore, we quantitatively analyze data association at the segment level, which underpins inter-image connectivity during mapping and segment-level localization when revisiting the same place. Finally, we show preliminary trials on segment-level ‘hopping’ based zero-shot real-world navigation. Project page with supplementary details: oravus.github.io/RoboHop/. Sourav Garg, Krishan Rana, Mehdi Hosseinzadeh 0003, Lachlan Mares, Niko Sünderhauf, Feras Dayoub, Ian D. Reid 0001 |
ICRA | 5 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 170 |
| 2024 | ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance FieldsabstractWhile existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline methods with an emphasis on visual fidelity, our paper addresses the online use case that prioritises real-time adaptability. We present ParticleNeRF, a new approach that dynamically adapts to changes in the scene geometry by learning an up-to-date representation online, every 200 ms. ParticleNeRF achieves this using a novel particle-based parametric encoding. We couple features to particles in space and backpropagate the photometric reconstruction loss into the particles’ position gradients, which are then interpreted as velocity vectors. Governed by a lightweight physics system to handle collisions, this lets the features move freely with the changing scene geometry. We demonstrate ParticleNeRF on various dynamic scenes containing translating, rotating, articulated, and deformable objects. ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other base-line approaches on dynamic scenes with online constraints. Videos of our system can be found at the anonymous project website https://sites.google.com/view/particlenerf. Jad Abou-Chakra, Feras Dayoub, Niko Sünderhauf |
WACV | 3 |
| 2023 | SAFE: Sensitivity-Aware Features for Out-of-Distribution Object DetectionabstractWe address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object, and train a multilayer perceptron on the surrogate task of distinguishing adversarially perturbed from clean in-distribution examples. This circumvents the need for realistic OOD training data, computationally expensive generative models, or retraining of the base object detector. SAFE outperforms the state-of-the-art OOD object detectors on multiple benchmarks by large margins, e.g. reducing the FPR95 by an absolute 30.6% from 48.3% to 17.7% on the OpenImages dataset. Tobias Fischer 0001, Feras Dayoub, Dimity Miller, Niko Sünderhauf |
ICCV | 5 |
| 2023 | Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance FieldsabstractWe show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered. The naive ensembles investigated in prior work simply average rendered RGB images to quantify the model uncertainty caused by conflicting explanations of the observed scene. In contrast, we additionally consider the termination probabilities along individual rays to identify epistemic model uncertainty due to a lack of knowledge about the parts of a scene unobserved during training. We achieve new state-of-the-art performance across established uncertainty quantification benchmarks for NeRFs, outperforming methods that require complex changes to the NeRF architecture and training regime. We furthermore demonstrate that NeRF uncertainty can be utilised for next-best view selection and model refinement. Niko Sünderhauf, Jad Abou-Chakra, Dimity Miller |
ICRA | 1 |
| 2023 | Hyperdimensional Feature Fusion for Out-of-Distribution DetectionabstractWe introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection. In contrast to most existing works that perform OOD detection based on only a single layer of a neural network, we use similarity-preserving semi-orthogonal projection matrices to project the feature maps from multiple layers into a common vector space. By repeatedly applying the bundling operation ⊕, we create expressive class-specific descriptor vectors for all in-distribution classes. At test time, a simple and efficient cosine similarity calculation between descriptor vectors consistently identifies OOD samples with competitive performance to the current state-of-the-art whilst being significantly faster. We show that our method is orthogonal to recent state-of-the-art OOD detectors and can be combined with them to further improve upon the performance. Tobias Fischer 0001, Niko Sünderhauf, Feras Dayoub |
WACV | 3 |
| 2022 | Forest Traversability Mapping (FTM): Traversability estimation using 3D voxel-based Normal Distributed Transform to enable forest navigationabstractAutonomous navigation in dense vegetation remains an open challenge and is an area of major interest for the research community. In this paper we propose a novel traversability estimation method, the Forest Traversability Map, that gives autonomous ground vehicles the ability to navigate in harsh forests or densely vegetated environments. The method estimates travers ability in unstructured environments dominated by vegetation, void of any dominant human structures, gravel or dirt roads, with higher accuracy than the state of the art: we demonstrate an improvement of over 20% F1 score (from 0.71 to 0.91) on challenging real-world data. Our method is based on 3D voxel representation and introduces a robust colour fusion method to overcome occlusion and frequent changes of lighting conditions in these environments. We also introduce and fuse multi-return lidar measurements into our probabilistic map representation in a recursive manner. Finally, we include information of neighboring voxels to increase our ability to assess the terrain travers ability correctly. These measures improve the state-of-the-art results and allow for effective traversability estimation in very challenging, densely vegetated environments. Fabio Ruetz, Paulo Vinicius Koerich Borges, Niko Sünderhauf, Emili Hernández, Thierry Peynot |
IROS | 3 |
| 2021 | VarifocalNet: An IoU-Aware Dense Object DetectorabstractAccurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-Aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ~2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. Code is available at: https://github.com/hyz-xmaster/VarifocalNet. Ying Wang 0074, Feras Dayoub, Niko Sünderhauf |
CVPR | 4 |
| 2021 | Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAMabstractRecent Semantic SLAM methods combine classical geometry-based estimation with deep learning-based object detection or semantic segmentation. In this paper we evaluate the quality of semantic maps generated by state-of-the-art class-and instance-aware dense semantic SLAM algorithms whose codes are publicly available and explore the impacts both semantic segmentation and pose estimation have on the quality of semantic maps. We obtain these results by providing algorithms with ground-truth pose and/or semantic segmentation data available from simulated environments. We establish that semantic segmentation is the largest source of error through our experiments, dropping mAP and OMQ performance by up to 74.3% and 71.3% respectively. Suman Raj Bista, David Hall 0003, Ben Talbot, Feras Dayoub, Niko Sünderhauf |
IROS | 6 |
| 2021 | Online Monitoring of Object Detection Performance During DeploymentabstractDuring deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector’s performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors. Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub |
IROS | 2 |
| 2021 | Class Anchor Clustering: A Loss for Distance-based Open Set RecognitionabstractIn open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classes by measuring distance in a network's logit space, assuming that known classes cluster closer to the training data than unknown classes. However, this approach is applied post-hoc to networks trained with cross-entropy loss, which does not guarantee this clustering behaviour. To overcome this limitation, we introduce the Class Anchor Clustering (CAC) loss. CAC is a distance-based loss that explicitly trains known classes to form tight clusters around anchored class-dependent centres in the logit space. We show that training with CAC achieves state-of-the-art performance for distance-based open set classifiers on all six standard benchmark datasets, with a 15.2% AUROC increase on the challenging TinyImageNet, without sacrificing classification accuracy. We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes. Dimity Miller, Niko Sünderhauf, Michael Milford, Feras Dayoub |
WACV | 2 |
| 2020 | Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown EnvironmentsabstractIn this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones. We present an extension of the residual reinforcement learning framework from the robotic manipulation literature and adapt it to the vast and unstructured environments that mobile robots can operate in. The concept is based on learning a residual control effect to add to a typical sub-optimal classical controller in order to close the performance gap, whilst guiding the exploration process during training for improved data efficiency. We exploit this tight coupling and propose a novel deployment strategy, switching Residual Reactive Navigation (sRRN), which yields efficient trajectories whilst probabilistically switching to a classical controller in cases of high policy uncertainty. Our approach achieves improved performance over end-to-end alternatives and can be incorporated as part of a complete navigation stack for cluttered indoor navigation tasks in the real world. The code and training environment for this project is made publicly available at https://sites.google.com/view/srrn/home. Krishan Rana, Ben Talbot, Vibhavari Dasagi, Michael Milford, Niko Sünderhauf |
ICRA | 5 |
| 2020 | Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real TransferabstractLearning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at https://sites.google.com/view/mcf-nav/home. Krishan Rana, Vibhavari Dasagi, Ben Talbot, Michael Milford, Niko Sünderhauf |
IROS | 5 |
| 2020 | Probabilistic Object Detection: Definition and EvaluationabstractWe introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the lack of methods capable of assessing such probabilistic object detections, we present the new Probability-based Detection Quality measure (PDQ). Unlike AP-based measures, PDQ has no arbitrary thresholds and rewards spatial and label quality, and foreground/background separation quality while explicitly penalising false positive and false negative detections. We contrast PDQ with existing mAP and moLRP measures by evaluating state-of-the-art detectors and a Bayesian object detector based on Monte Carlo Dropout. Our experiments indicate that conventional object detectors tend to be spatially overconfident and thus perform poorly on the task of probabilistic object detection. Our paper aims to encourage the development of new object detection approaches that provide detections with accurately estimated spatial and label uncertainties and are of critical importance for deployment on robots and embodied AI systems in the real world. David Hall 0003, Feras Dayoub, John Skinner, Dimity Miller, Peter I. Corke, Gustavo Carneiro 0001, Anelia Angelova, Niko Sünderhauf |
WACV | 9 |
| 2020 | Special Issue on Deep Learning for Robotic Vision
Anelia Angelova, Gustavo Carneiro 0001, Niko Sünderhauf, Jürgen Leitner |
Int. J. Comput. Vis. | 3 |
| 2019 | Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth EstimationabstractVisual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Particularly challenging is the scenario where both phenomena occur simultaneously, such as when returning for the first time along a road at night that was previously traversed during the day in the opposite direction. While such problems can be solved with panoramic sensors, humans solve this problem regularly with limited field-of-view vision and without needing to constantly turn around. In this paper, we present a new depth- and temporal-aware visual place recognition system that solves the opposing viewpoint, extreme appearance-change visual place recognition problem. Our system performs sequence-to-single frame matching by extracting depth-filtered keypoints using a state-of-the-art depth estimation pipeline, constructing a keypoint sequence over multiple frames from the reference dataset, and comparing these keypoints to the keypoints extracted from a single query image. We evaluate the system on a challenging benchmark dataset and show that it consistently outperforms state-of-the-art techniques. We also develop a range of diagnostic simulation experiments that characterize the contribution of depth-filtered keypoint sequences with respect to key domain parameters including the degree of appearance change and camera motion. Sourav Garg, V. Madhu Babu, Thanuja Dharmasiri, Stephen Hausler, Niko Sünderhauf, Swagat Kumar, Tom Drummond, Michael Milford |
ICRA | 5 |
| 2019 | Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object DetectionabstractThere has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategies. We compare different combinations of three spatial and two semantic affinity measures with four clustering methods for MC Dropout with a Single Shot Multi-Box Detector. Our results show that the correct choice of affinity-clustering combination can greatly improve the effectiveness of the classification and spatial uncertainty estimation and the resulting object detection performance. We base our evaluation on a new mix of datasets that emulate near open-set conditions (semantically similar unknown classes), distant open-set conditions (semantically dissimilar unknown classes) and the common closed-set conditions (only known classes). Dimity Miller, Feras Dayoub, Michael Milford, Niko Sünderhauf |
ICRA | 4 |
| 2019 | Did You Miss the Sign? A False Negative Alarm System for Traffic Sign DetectorsabstractObject detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic sign as an object plays a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we are proposing an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life. Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub |
IROS | 2 |
| 2019 | What Can Robotics Research Learn from Computer Vision Research?
Peter I. Corke, Feras Dayoub, David Hall 0003, John Skinner, Niko Sünderhauf |
ISRR | 5 |
| 2018 | Structure Aware SLAM Using Quadrics and Planes
Mehdi Hosseinzadeh 0003, Yasir Latif, Trung Pham, Niko Sünderhauf, Ian D. Reid 0001 |
ACCV (3) | 4 |
| 2018 | Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real EnvironmentsabstractA robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1. Peter Anderson 0001, Qi Wu 0001, Damien Teney, Jake Bruce, Mark Johnson 0001, Niko Sünderhauf, Ian D. Reid 0001, Stephen Gould, Anton van den Hengel |
CVPR | 6 |
| 2018 | Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place RecognitionabstractWhen a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as “passing a shop on the left” or “moving through a forested area”. Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets - one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-Iook behaviours, and compare to existing state-of-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components: the use of spatio-temporal sequences, place categorization and place-centric characteristics as opposed to object-centric semantics. Sourav Garg, Niko Sünderhauf, Michael Milford |
ICRA | 2 |
| 2018 | Dropout Sampling for Robust Object Detection in Open-Set ConditionsabstractDropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We evaluate this approach on a large synthetic dataset of 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. A Dropout Sampling network is shown to achieve a 12.3 % increase in recall (for the same precision score as a standard network) and a 15.1 % increase in precision (for the same recall score as the standard network). Dimity Miller, Lachlan Nicholson, Feras Dayoub, Niko Sünderhauf |
ICRA | 4 |
| 2018 | SceneCut: Joint Geometric and Object Segmentation for Indoor ScenesabstractThis paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image. SceneCut's joint reasoning over scene semantics and geometry allows a robot to detect and segment object instances in complex scenes where modern deep learning-based methods either fail to separate object instances, or fail to detect objects that were not seen during training. SceneCut automatically decomposes a scene into meaningful regions which either represent objects or scene surfaces. The decomposition is qualified by an unified energy function over objectness and geometric fitting. We show how this energy function can be optimized efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage a pre-trained convolutional oriented boundary network to predict accurate boundaries from images, which are used to construct high-quality region hierarchies. We evaluate SceneCut on several different indoor environments, and the results show that SceneCut significantly outperforms all the existing methods. Trung T. Pham, Thanh-Toan Do, Niko Sünderhauf, Ian D. Reid 0001 |
ICRA | 3 |
| 2017 | Deep learning features at scale for visual place recognitionabstractThe success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area. Zetao Chen, Adam Jacobson, Niko Sünderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian D. Reid 0001, Michael Milford |
ICRA | 3 |
| 2017 | The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible researchabstractRobotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems - including perception and manipulation - instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot. Jürgen Leitner, Adam W. Tow, Niko Sünderhauf, Jake E. Dean, Joseph W. Durham, Matthew Cooper 0005, Markus Eich, Chris Lehnert, Ruben Mangels, Chris McCool, Peter Kujala, Lachlan Nicholson, Trung Pham, James Sergeant, Liao Wu, Fangyi Zhang, Ben Upcroft, Peter I. Corke |
ICRA | 3 |
| 2017 | Meaningful maps with object-oriented semantic mappingabstractFor intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation. Niko Sünderhauf, Trung T. Pham, Yasir Latif, Michael Milford, Ian D. Reid 0001 |
IROS | 1 |
| 2016 | Place categorization and semantic mapping on a mobile robotabstractIn this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module. Niko Sünderhauf, Feras Dayoub, Sean McMahon, Ben Talbot, Ruth Schulz, Peter I. Corke, Gordon F. Wyeth, Ben Upcroft, Michael Milford |
ICRA | 1 |
| 2016 | High-fidelity simulation for evaluating robotic vision performanceabstractRobotic vision, unlike computer vision, typically involves processing a stream of images from a camera with time varying pose operating in an environment with time varying lighting conditions and moving objects. Repeating robotic vision experiments under identical conditions is often impossible, making it difficult to compare different algorithms. For machine learning applications a critical bottleneck is the limited amount of real world image data that can be captured and labelled for both training and testing purposes. In this paper we investigate the use of a photo-realistic simulation tool to address these challenges, in three specific domains: robust place recognition, visual SLAM and object recognition. For the first two problems we generate images from a complex 3D environment with systematically varying camera paths, camera viewpoints and lighting conditions. For the first time we are able to systematically characterise the performance of these algorithms as paths and lighting conditions change. In particular, we are able to systematically generate varying camera viewpoint datasets that would be difficult or impossible to generate in the real world. We also compare algorithm results for a camera in a real environment and a simulated camera in a simulation model of that real environment. Finally, for the object recognition domain, we generate labelled image data and characterise the viewpoint dependency of a current convolution neural network in performing object recognition. Together these results provide a multi-domain demonstration of the beneficial properties of using simulation to characterise and analyse a wide range of robotic vision algorithms. John Skinner, Sourav Garg, Niko Sünderhauf, Peter I. Corke, Ben Upcroft, Michael Milford |
IROS | 3 |
| 2016 | Visual Place Recognition: A SurveyabstractVisual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description. Stephanie M. Lowry, Niko Sünderhauf, Paul Newman 0001, John J. Leonard, David D. Cox, Peter I. Corke, Michael Milford |
IEEE Trans. Robotics | 2 |
| 2015 | On the performance of ConvNet features for place recognitionabstractAfter the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different. This paper comprehensively evaluates and compares the utility of three state-of-the-art ConvNets on the problems of particular relevance to navigation for robots; viewpoint-invariance and condition-invariance, and for the first time enables real-time place recognition performance using ConvNets with large maps by integrating a variety of existing (locality-sensitive hashing) and novel (semantic search space partitioning) optimization techniques. We present extensive experiments on four real world datasets cultivated to evaluate each of the specific challenges in place recognition. The results demonstrate that speed-ups of two orders of magnitude can be achieved with minimal accuracy degradation, enabling real-time performance. We confirm that networks trained for semantic place categorization also perform better at (specific) place recognition when faced with severe appearance changes and provide a reference for which networks and layers are optimal for different aspects of the place recognition problem. Niko Sünderhauf, Sareh Rowlands, Feras Dayoub, Ben Upcroft, Michael Milford |
IROS | 1 |
| 2015 | Evaluation of Features for Leaf Classification in Challenging ConditionsabstractFine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (Conv Net) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and Conv Net features yields state-of-the art performance with an average accuracy of 97.3%±0:6% compared to traditional features which obtain an average accuracy of 91.2%±1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5.7% for all of the evaluated condition variations. David Hall 0003, Chris McCool, Feras Dayoub, Niko Sünderhauf, Ben Upcroft |
WACV | 4 |
| 2013 | Incremental smoothing vs. filtering for sensor fusion on an indoor UAVabstractOur paper explores the performance of a recently proposed incremental smoother in the context of nonlinear sensor fusion for a real-world UAV. This efficient factor graph based smoothing approach has a number of advantages compared to conventional filtering techniques like the EKF or its variants. It can more easily incorporate asynchronous and delayed measurements from sensors operating at different rates and is supposed to be less error-prone in highly nonlinear settings. We compare the novel incremental smoothing approach based on iSAM2 against our conventional EKF based sensor fusion framework. Unlike previously presented work, the experiments are not only performed in simulation, but also on a real-world quadrotor UAV system using IMU, optical flow and altitude measurements. Sven Lange, Niko Sünderhauf, Peter Protzel |
ICRA | 2 |
| 2013 | Switchable constraints vs. max-mixture models vs. RRR - A comparison of three approaches to robust pose graph SLAMabstractSLAM algorithms that can infer a correct map despite the presence of outliers have recently attracted increasing attention. In the context of SLAM, outlier constraints are typically caused by a failed place recognition due to perceptional aliasing. If not handled correctly, they can have catastrophic effects on the inferred map. Since robust robotic mapping and SLAM are among the key requirements for autonomous long-term operation, inference methods that can cope with such data association failures are a hot topic in current research. Our paper compares three very recently published approaches to robust pose graph SLAM, namely switchable constraints, max-mixture models and the RRR algorithm. All three methods were developed as extensions to existing factor graph-based SLAM back-ends and aim at improving the overall system's robustness to false positive loop closure constraints. Due to the novelty of the three proposed algorithms, no direct comparison has been conducted so far. Niko Sünderhauf, Peter Protzel |
ICRA | 1 |
| 2013 | Switchable constraints and incremental smoothing for online mitigation of non-line-of-sight and multipath effectsabstractReliable vehicle positioning is a crucial requirement for many applications of advanced driver assistance systems. While satellite navigation provides a reasonable performance in general, it often suffers from multipath and non-line-of-sight errors when it is applied in urban areas and therefore does not guarantee consistent results anymore. Our paper proposes a novel online method that identifies and excludes the affected pseudorange measurements. Our approach does not depend on additional sensors, maps, or environmental models. We rather formulate the positioning problem as a Bayesian inference problem in a factor graph and combine the recently developed concept of switchable constraints with an algorithm for efficient incremental inference in such graphs. We furthermore introduce the concepts of auxiliary updates and factor graph pruning in order to accelerate convergence while keeping the graph size and required runtime bounded. A real-world experiment demonstrates that the resulting algorithm is able to successfully localize despite a large number of satellite observations are influenced by NLOS or multipath effects. Niko Sünderhauf, Marcus Obst, Sven Lange, Gerd Wanielik, Peter Protzel |
Intelligent Vehicles Symposium | 1 |
| 2012 | Towards a robust back-end for pose graph SLAMabstractCurrent state of the art solutions of the SLAM problem are based on efficient sparse optimization techniques and represent the problem as probabilistic constraint graphs. For example in pose graphs the nodes represent poses and the edges between them express spatial information (e.g. obtained from odometry) and information on loop closures. The task of constructing the graph is delegated to a front-end that has access to the available sensor information. The optimizer, the so called back-end of the system, relies heavily on the topological correctness of the graph structure and is not robust against misplaced constraint edges. Especially edges representing false positive loop closures will lead to the divergence of current solvers. We propose a novel formulation that allows the back-end to change parts of the topological structure of the graph during the optimization process. The back-end can thereby discard loop closures and converge towards correct solutions even in the presence of false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. We demonstrate the approach and present results both on large scale synthetic and real-world datasets. Niko Sünderhauf, Peter Protzel |
ICRA | 1 |
| 2012 | Switchable constraints for robust pose graph SLAMabstractCurrent SLAM back-ends are based on least squares optimization and thus are not robust against outliers like data association errors and false positive loop closure detections. Our paper presents and evaluates a robust back-end formulation for SLAM using switchable constraints. Instead of proposing yet another appearance-based data association technique, our system is able to recognize and reject outliers during the optimization. This is achieved by making the topology of the underlying factor graph representation subject to the optimization instead of keeping it fixed. The evaluation shows that the approach can deal with up to 1000 false positive loop closure constraints on various datasets. This largely increases the robustness of the overall SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. Niko Sünderhauf, Peter Protzel |
IROS | 1 |
| 2012 | Multipath mitigation in GNSS-based localization using robust optimizationabstractOur paper adapts recent advances in the SLAM (Simultaneous Localization and Mapping) literature to the problem of multipath mitigation and proposes a novel approach to successfully localize a vehicle despite a significant number of multipath observations. We show that GNSS-based localization problems can be modelled as factor graphs and solved using efficient nonlinear least squares methods that exploit the sparsity inherent in the problem formulation. Using a recently developed novel approach for robust optimization, satellite observations that are subject to multipath errors can be successfully identified and rejected during the optimization process. We demonstrate the feasibility of the proposed approach on a real-world urban dataset and compare it to an existing method of multipath detection. Niko Sünderhauf, Marcus Obst, Gerd Wanielik, Peter Protzel |
Intelligent Vehicles Symposium | 1 |
| 2011 | BRIEF-Gist - Closing the loop by simple meansabstractThe ability to recognize known places is an essential competence of any intelligent system that operates autonomously over longer periods of time. Approaches that rely on the visual appearance of distinct scenes have recently been developed and applied to large scale SLAM scenarios. FAB-Map is maybe the most successful of these systems. Our paper proposes BRIEF-Gist, a very simplistic appearance-based place recognition system based on the BRIEF descriptor. BRIEF-Gist is much more easy to implement and more efficient compared to recent approaches like FAB-Map. Despite its simplicity, we can show that it performs comparably well as a front-end for large scale SLAM. We benchmark our approach using two standard datasets and perform SLAM on the 66 km long urban St. Lucia dataset. Niko Sünderhauf, Peter Protzel |
IROS | 1 |
| 2010 | Beyond RatSLAM: Improvements to a biologically inspired SLAM systemabstractA SLAM algorithm inspired by biological principles has been recently proposed and shown to perform well in a large and demanding scenario. We analyse and compare this system (RatSLAM) and the established Bayesian SLAM methods and identify the key difference to be an additive update step. Using this insight, we derive a novel filter scheme and successfully show that it can entirely replace the core of the RatSLAM system while maintaining its desirable robustness. This leads to a massive speedup, as the novel filter can be calculated very efficiently. We successfully applied the new algorithm to the same 66 km long dataset that was used with the original algorithm. Niko Sünderhauf, Peter Protzel |
ETFA | 1 |
| 2010 | The causal update filter - A novel biologically inspired filter paradigm for appearance-based SLAMabstractRecently a SLAM algorithm based on biological principles (RatSLAM) has been proposed. It was proven to perform well in large and demanding scenarios. In this paper we establish a comparison of the principles underlying this algorithm with standard probabilistic SLAM approaches and identify the key difference to be an additive update step. Using this insight, we derive the novel, non-Bayesian Causal Update filter that is suitable for application in appearance-based SLAM. We successfully apply this new filter to two demanding vision-only urban SLAM problems of 5 and 66 km length. We show that it can functionally replace the core of RatSLAM, gaining a massive speed-up. Niko Sünderhauf, Peer Neubert, Peter Protzel |
IROS | 1 |
| 2006 | Bringing Robotics closer to Students - a Threefold ApproachabstractIn this paper, we present our threefold concept of "bringing robotics closer to students". Our efforts begin with motivating high-school students to study engineering sciences by increasing their interest in technical issues. This is achieved by a national robotics contest. Besides our work with high-school students, we also focus on projects for undergraduate university students. Several robotics projects and competitions are included into the curriculum of electrical and mechanical engineering and computer science. These projects offer chances to do special assignments and student research projects. It may be important to point out that most of the work described in this paper, including inventing and specifying the projects and organizing the events, has been done by undergraduate and graduate students Niko Sünderhauf, Thomas Krause 0002, Peter Protzel |
ICRA | 1 |
| 2005 | RoboKing - Bringing Robotics closer to PupilsabstractIn this paper, we introduce RoboKing, a national contest of mobile autonomous robots, dedicated to teams of high school students. RoboKing differs from similar contests by supporting the participating teams with a 250 Euro voucher and by not restricting the kind of materials the robots can be build with. Its task is manageable for students without previous knowledge in robotics but offers enough complexity to be challenging for advanced participants. The first RoboKing contest with 12 participating teams from different parts of Germany took place at the Hannover Messe in 2004. Because of its great success, RoboKing will be held annually. RoboKing 2005 has been extended to 20 teams of pupils and offers a new challenging task. More pictures and video files can be found under www. roboking. de . Niko Sünderhauf, Thomas Krause 0002, Peter Protzel |
ICRA | 1 |