Rapid and reliable identification of dynamic scene parts, also known as motion segmentation, is a key challenge for mobile sensors. Contemporary RGB camera-based methods rely on modeling camera and scene properties however, are often under-constrained and fall short in unknown categories. Event cameras have the potential to overcome these limitations, but corresponding methods have only been demonstrated in smaller-scale indoor environments with simplified dynamic objects. This work presents an event-based method for class-agnostic motion segmentation that can successfully be deployed across complex large-scale outdoor environments too. To this end, we introduce a novel divide-and-conquer pipeline that combines: (a) ego-motion compensated events, computed via a scene understanding module that predicts monocular depth and camera pose as auxiliary tasks, and (b) optical flow from a dedicated optical flow module. These intermediate representations are then fed into a segmentation module that predicts motion segmentation masks. A novel transformer-based temporal attention module in the segmentation module builds correlations across adjacent ‘frames’ to get temporally consistent segmentation masks. Our method sets the new state-of-the-art on the classic EV-IMO benchmark (indoors), where we achieve improvements of 2.19 moving object IoU (2.22 mIoU) and 4.52 point IoU respectively, as well as on a newly-generated motion segmentation and tracking benchmark (outdoors) based on the DSEC event dataset, termed DSEC-MOTS, where we show improvement of 12.91 moving object IoU.

Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird’s-Eye-View (BEV) segmentation allow for accurate estimation of local scene appearance and in turn, can benefit the relocalization of the vehicle. However, one downside of BEV methods is the heavy computation required to leverage the geometric constraints. This paper presents U-BEV, a U-Net inspired architecture that extends the current state-of-the-art by allowing the BEV to reason about the scene on multiple height layers before flattening the BEV features. We show that this extension boosts the performance of the U-BEV by up to 4.11 IoU. Additionally, we combine the encoded neural BEV with a differentiable template matcher to perform relocalization on neural SD-map data. The model is fully end-to-end trainable and outperforms transformer-based BEV methods of similar computational complexity by 1.7 to 2.8 mIoU and BEV-based relocalization by over 26% Recall Accuracy on the nuScenes dataset.

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation.Sampling-basedtechniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together withcontrol barrier functionsandpassivity theorythat, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.

Suitable datasets are an integral part of robotics research, especially for training neural networks in robot perception. However, in many domains, suitable real-world data are scarce and cannot be easily obtained. This problem is especially prevalent for unstructured outdoor environments, in particular, planetary ones. Recent advances in photorealistic simulations help researchers to simulate close-to-real data in many domains. Yet, there exists no high-quality synthetic data for planetary exploration tasks. Also, existing simulators lack the fidelity required for generating planetary data, which is inherently less structured than human environments. Synthetic planetary data requires careful modeling and annotation of many different terrain aspect and details, such as textures and distributions of rocks, to become a valuable test-bed for robotics. To fill this gap, we present a novel simulator specifically designed for the needs of planetary robotics visual tasks, but also applicable for other outdoor environments. Our simulator is capable of generating large varieties of (planetary) outdoor scenes with rich generation of meta data, such as multilevel semantic and instance annotations. To demonstrate the wide applicability of this new simulator, we evaluate its performance on typical robotics applications, i.e. semantic segmentation, instance segmentation, and visual SLAM. Our simulator is accessible under https://github.com/DLR-RM/oaisys.

This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3 m/s to 12 m/s while still mapping with an acceptable RMSE of 0.29 m.

In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences. To limit the impact of such failures, this paper presents the redundant perception and state estimation approaches developed for an autonomous race car. Redundancy in perception is achieved by estimating the color and position of the track delimiting objects using two sensor modalities independently. Specifically, learning-based approaches are used to generate color and pose estimates, from LiDAR and camera data respectively. The redundant perception inputs are fused by a particle filter based SLAM algorithm that operates in real-time. Velocity is estimated using slip dynamics, with reliability being ensured through a probabilistic failure detection algorithm. The sub-modules are extensively evaluated in real-world racing conditions using the autonomous race car gotthard driverless, achieving lateral accelerations up to 1. 7G and a top speed of 90km/h.

We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites. The approach leverages multi-modal sensing capabilities for state estimation, tight integration with digital building models, and integrated trajectory planning and whole-body motion control. A novel method for high-accuracy localization updates relative to the known building structure is proposed. The approach is implemented on a real platform and tested under realistic construction conditions. We show that the system can achieve sub-cm end-effector positioning accuracy during fully autonomous operation using solely onboard sensing.

Many robotic applications, such as search-and-rescue, require multiple agents to search for and perform actions on targets. However, such missions present several challenges, including cooperative exploration, task selection and allocation, time limitations, and computational complexity. To address this, we propose a decentralized multi-agent decision-making framework for the search and action problem with time constraints. The main idea is to treat time as an allocated budget in a setting where each agent action incurs a time cost and yields a certain reward. Our approach leverages probabilistic reasoning to make near-optimal decisions leading to maximized reward. We evaluate our method in the search, pick, and place scenario of the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), by using a probability density map and reward prediction function to assess actions. Extensive simulations show that our algorithm outperforms benchmark strategies, and we demonstrate system integration in a Gazebo-based environment, validating the framework's readiness for field application.

This paper introduces jlüela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of flüela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.

Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations. Current multi-sensor deep learning based semantic segmentation approaches do not provide robustness to under-performing classes in one modality, or require a specific architecture with access to the full aligned multi-sensor training data. In this work, we analyze statistical fusion approaches for semantic segmentation that overcome these drawbacks while keeping a competitive performance. The studied approaches are modular by construction, allowing to have different training sets per modality and only a much smaller subset is needed to calibrate the statistical models. We evaluate a range of statistical fusion approaches and report their performance against state-of-the-art baselines on both realworld and simulated data. In our experiments, the approach improves performance in IoU over the best single modality segmentation results by up to 5%. We make all implementations and configurations publicly available.

Collecting 3D object data sets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.

Autonomous delivery of goods using a Micro Air Vehicle (MAV) is a difficult problem, as it poses high demand on the MAV's control, perception and manipulation capabilities. This problem is especially challenging if the exact shape, location and configuration of the objects are unknown. In this paper, we report our findings during the development and evaluation of a fully integrated system that is energy efficient and enables MAVs to pick up and deliver objects with partly ferrous surface of varying shapes and weights. This is achieved by using a novel combination of an electro-permanent magnetic gripper with a passively compliant structure and integration with detection, control and servo positioning algorithms. The system's ability to grasp stationary and moving objects was tested, as well as its ability to cope with different shapes of the object and external disturbances. We show that such a system can be successfully deployed in scenarios where an object with partly ferrous parts needs to be gripped and placed in a predetermined location.

Using multiple cooperative robots is advantageous for time critical Search and Rescue (SaR) missions as they permit rapid exploration of the environment and provide higher redundancy than using a single robot. A considerable number of applications such as autonomous driving and disaster response could benefit from merging mapping data from several agents. Online multi-robot localization and mapping has mainly been addressed for robots equipped with cameras or 2D LiDARs. However, in unstructured and ill-lighted real-life scenarios, a mapping system can potentially benefit from a rich 3D geometric solution. In this work, we present an online localization and mapping system for multiple robots equipped with 3D LiDARs. This system is based on incremental sparse pose-graph optimization using sequential and place recognition constraints, the latter being identified using a 3D segment matching approach. The result is a unified representation of the world and relative robot trajectories. The complete system runs in real-time and is evaluated with two experiments in different environments: one urban and one disaster scenario. The system is available open source and easy-to-run demonstrations are publicly available.

Place recognition is a core component in simultaneous localization and mapping (SLAM), limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic applications demand increasingly higher accuracies. A large number of place recognition algorithms have been proposed, capable of consuming a variety of sensor data including laser, sonar and depth readings. The best performing solutions, however, have utilized visual information by either matching entire images or parts thereof. Most commonly, vision based approaches are inspired by information retrieval and utilize 3D-geometry information about the observed scene as a post-verification step. In this paper we propose to use the 3D-scene information from sparse-visual feature maps directly at the core of the place recognition pipeline. We propose a novel structural descriptor which aggregates sparse triangulated landmarks from SLAM into a compact signature. The resulting 3D-features provide a discriminative fingerprint to recognize places over seasonal and viewpoint changes which are particularly challenging for approaches based on sparse visual descriptors. We evaluate our system on publicly available datasets and show how its complementary nature can provide an improvement over visual place recognition.

Sliding window estimation is widely used for online simultaneous localization and mapping. While increasing the sliding window size generally yields improved accuracy, it also comes at an increase in computational cost. In order to reduce this cost, we propose smarter non-uniform sampling of the trajectory representation over the sliding window. This non-uniform temporal resolution is possible with continuous-time representations that allow freely adjustable knots location. Four strategies for selecting the knots location are presented and evaluated based on a real data laser-odometry SLAM problem. The results clearly show that non-uniform distributions of knots can be superior to uniform distribution in terms of accuracy per computation time.

Persistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance-based features which may suffer from lighting and viewpoint changes or require pre-registration between all sensor modalities used. This work presents a framework using structural descriptors for matching LIDAR point-cloud maps and sparse vision keypoint maps. The matching algorithm works independently of the sensors' viewpoint and varying lighting and does not require pre-registration between the sensors used. Furthermore, we employ the approach in a novel vision-laser map-merging algorithm. We analyse a range of structural descriptors and present results of the method integrated within a full mapping framework. Despite the fact that we match between the visual and laser domains, we can successfully perform map-merging using structural descriptors. The effectiveness of the presented structure-based vision-laser matching is evaluated on the public KITTI dataset and furthermore demonstrated on a map merging problem in an industrial site.