Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Conventional learning-based methods generally try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties. In this work, we introduce a novel approach for solving the task of uncertainty- aware panoptic segmentation using LiDAR point clouds. Our proposed EvLPSNet network is the first to solve this task efficiently in a sampling-free manner. It aims to predict per-point semantic and instance segmentations, together with per-point uncertainty estimates. Moreover, it incorporates methods that utilize the uncertainties to improve the segmentation performance. We provide several strong baselines combining state-of- the-art LiDAR panoptic segmentation networks with sampling- free uncertainty estimation techniques. Extensive evaluations show that we achieve the best performance on uncertainty- aware panoptic segmentation quality and calibration compared to these baselines. We make our code available at: https://github.com/kshitij3112/EvLPSNet

Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular localization approach based on a sliding-window pose graph that leverages predicted uncertainties for increased precision and robustness against challenging scenarios and per-frame failures. To this end, we propose an efficient multi-task uncertainty-aware perception module, which covers semantic segmentation, as well as bounding box detection, to enable the localization of vehicles in sparse maps, containing only lane borders and traffic lights. Further, we design differentiable cost maps that are directly generated from the estimated uncertainties. This opens up the possibility to minimize the reprojection loss of amorphous map elements in an association-free and uncertainty-aware manner. Extensive evaluation on the Lyft 5 dataset shows that, despite the sparsity of the map, our approach enables robust and accurate 6D localization in challenging urban scenarios using only monocular camera images and vehicle odometry.

Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing top–down approaches tackle this problem by either combining independent task-specific networks or translating methods from the image domain ignoring the intricacies of LiDAR data and thus often resulting in suboptimal performance. In this article, we present the novel top–down efficient LiDAR panoptic segmentation (EfficientLPS) architecture that addresses multiple challenges in segmenting LiDAR point clouds, including distance-dependent sparsity, severe occlusions, large scale-variations, and reprojection errors. EfficientLPS comprises of a novel shared backbone that encodes with strengthened geometric transformation modeling capacity and aggregates semantically rich range-aware multiscale features. It incorporates new scale-invariant semantic and instance segmentation heads along with the panoptic fusion module which is supervised by our proposed panoptic periphery loss function. Additionally, we formulate a regularized pseudolabeling framework to further improve the performance of EfficientLPS by training on unlabeled data. We benchmark our proposed model on two large-scale LiDAR datasets: nuScenes, for which we also provide ground truth annotations, and SemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both these datasets.

Horizontally scanning 2D laser rangefinders are a popular approach for indoor robot localization because of the high accuracy of the sensors and the compactness of the required 2D maps. As the scanners in this configuration only provide information about one slice of the environment, the measurements typically do not capture the full extent of a large variety of obstacles, including chairs or tables. Accordingly, obstacle avoidance based on laser scanners mounted in such a fashion is likely to fail. In this paper, we propose a learning-based approach to predict collisions in 2D occupancy maps. Our approach is based on a convolutional neural network which is trained on a 2D occupancy map and collision events recorded with a bumper while the robot is navigating in its environment. As the network operates on local structures only, it can generalize to new environments. In addition, the robot can collect and integrate new collision examples after an initial training phase. Extensive experiments carried out in simulation and a realistic real-world environment confirm that our approach allows robots to learn from collision events to avoid collisions in the future.

Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal traffic schedules. In this paper we present an approach to learn policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow. Our method uses a novel formulation of the reward function that simultaneously considers efficiency and equity. We furthermore present a general approach to find the bound for the proposed equity factor and we introduce the adaptive discounting approach that greatly stabilizes learning and helps to maintain a high flexibility of green light duration. The experimental evaluations on both simulated and real-world data demonstrate that our proposed algorithm achieves state-of-the-art performance (previously held by traditional non-learning methods) on a wide range of traffic situations.

Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we propose a strictly probabilistic method to detect finite planes in organized 3-D laser range scans. An agglomerative hierarchical clustering technique, our algorithm builds planes from bottom up, always extending a plane by the point that decreases the measurement likelihood of the scan the least. In contrast to most related methods, which rely on heuristics like orthogonal point-to-plane distance, we leverage the ray path information to compute the measurement likelihood. We evaluate our approach not only on the popular SegComp benchmark, but also provide a challenging synthetic dataset that overcomes SegComp's deficiencies. Both our implementation and the suggested dataset are available at [1].

Most of the conventional approaches to mobile robot navigation avoid any kind of contact with the environment or with humans. As nowadays distance sensors typically have a limited - and often only two-dimensional - field of view, collisions with the environment or contacts with humans cannot be fully avoided in practical mobile robot applications. On the other hand, direct physical contact can be used for intuitive communication between a robot and humans. In this paper, we present a whole-body sensory concept based on a 6-DoF force-torque sensor to perceive physical interaction between the robot and humans. To distinguish between external contact and disturbance forces that result from the motion of the mobile platform or oscillations, we present a novel model-free filtering approach based on a neural network. In extensive experiments carried out with our robot Canny we demonstrate the effectiveness and advantages of the neural network approach, which clearly outperforms a classical model-based one.

The ability to build models of the environment is an essential prerequisite for many robotic applications. In recent years, mapping of dense surface geometry using RGB-D cameras has seen extensive progress. Many approaches build colored models, typically directly using the intensity values provided by the camera. Unfortunately, these intensities are inherently affected by illumination. Therefore, the resulting maps only represent the environment for one specific lighting condition. To overcome this limitation, we propose to build reflectance maps that are invariant against changes in lighting. Our approach estimates the diffuse reflectance of a surface by recovering its radiosity and the corresponding irradiance. As imperfections in this process can significantly degrade the reflectance estimate, we remove outliers in the high dynamic range radiosity estimation and propose a method to refine the reflectance estimate. Our system implements the whole pipeline for offline reconstruction of dense reflectance maps including the segmentation of light emitters in the scene. We demonstrate the applicability of our approach in real-world experiments under varying lighting conditions.

Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent their geometry. In this paper, we propose a novel probabilistic method to extract polylines from raw 2-D laser range scans. The key idea of our approach is to determine a set of polylines that maximizes the likelihood of a given scan. In extensive experiments carried out on publicly available real-world datasets and on simulated laser scans, we demonstrate that our method substantially outperforms existing state-of-the-art approaches in terms of accuracy, while showing comparable computational requirements. Our implementation is available under https://github.com/acschaefer/ple.