Robust and accurate point cloud registration is an essential part of many robotic tasks such as SLAM or object pose retrieval. In this paper, we address the problem of global 3D point cloud registration, i.e., the task of estimating the 3D rigid body transform between a source and a target point cloud without any initial guess. Typically, the problem is solved by extracting and matching features to find a data association and then computing a transform that minimizes the squared distance between points. Our approach combines the normal distributions transform and oriented point pair framework and introduces the NDT distance histogram to quickly generate and test candidate transforms. Our method further exploits semantic information if available for greater speed. We implement our algorithm in C++ and compare it to other state-of-the-art approaches on a diverse set of environments. Our evaluation shows that our method outperforms the other approaches, especially concerning run-time and compute efficiency.

Advances in sensing and learning algorithms have led to increasingly mature solutions for human detection by robots, particularly in selected use-cases such as pedestrian detection for self-driving cars or close-range person detection in consumer settings. Despite this progress, the simple question which sensor-algorithm combination is best suited for a person detection task at handƒ remains hard to answer. In this paper, we tackle this issue by conducting a systematic cross-modal analysis of sensor-algorithm combinations typically used in robotics. We compare the performance of state-of-the-art person detectors for 2D range data, 3D lidar, and RGB-D data as well as selected combinations thereof in a challenging industrial use-case.We further address the related problems of data scarcity in the industrial target domain, and that recent research on human detection in 3D point clouds has mostly focused on autonomous driving scenarios. To leverage these methodological advances for robotics applications, we utilize a simple, yet effective multi-sensor transfer learning strategy by extending a strong image-based RGB-D detector to provide cross-modal supervision for lidar detectors in the form of weak 3D bounding box labels.Our results show a large variance among the different approaches in terms of detection performance, generalization, frame rates and computational requirements. As our use-case contains difficulties representative for a wide range of service robot applications, we believe that these results point to relevant open challenges for further research and provide valuable support to practitioners for the design of their robot system.

While 2D object detection has made significant progress, robustly localizing objects in 3D space under presence of occlusion is still an unresolved issue. Our focus in this work is on real-time detection of human 3D centroids in RGB-D data. We propose an image-based detection approach which extends the YOLO v3 architecture with a 3D centroid loss and mid-level feature fusion to exploit complementary information from both modalities. We employ a transfer learning scheme which can benefit from existing large-scale 2D object detection datasets, while at the same time learning end-to-end 3D localization from our highly randomized, diverse synthetic RGB-D dataset with precise 3D groundtruth. We further propose a geometrically more accurate depth-aware crop augmentation for training on RGB-D data, which helps to improve 3D localization accuracy. In experiments on our challenging intralogistics dataset, we achieve state-of-the-art performance even when learning 3D localization just from synthetic data.

For safety reasons, robotic lawn mowers and similar devices are required to stay within a predefined working area. Keeping the robot within its workspace is typically achieved by special safeguards such as a wire installed in the ground. In the case of robotic lawn mowers, this causes a certain customer reluctance. It is more desirable to fulfill those safety-critical tasks by safe navigation and path planning. In this paper, we tackle the problem of planning a coverage path composed of parallel lanes that maximizes robot safety under the constraints of cheap, low range sensors and thus substantial uncertainty in the robot's belief and ability to execute actions. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for an uncertainty-aware coverage path while avoiding collisions. We implemented our approach using C++ and ROS and thoroughly tested it on real garden data. The experiment shows that our approach leads to safer meander patterns for the lawn mower and takes expected localizability information into account.

Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short computation times and short paths while maximizing robot safety under certain assumptions.