Autonomously driving vehicles must be able to navigate in dynamic and unpredictable environments in a collision-free manner. So far, this has only been partially achieved in driverless cars and warehouse installations where marked structures such as roads, lanes, and traffic signs simplify the motion planning and collision avoidance problem. We are presenting a new control approach for car-like vehicles that is based on an unprecedentedly fast-paced A* implementation that allows the control cycle to run at a frequency of 30 Hz. This frequency enables us to place our A* algorithm as a low-level replanning controller that is well suited for navigation and collision avoidance in virtually any dynamic environment. Due to an efficient heuristic consisting of rotate-translate-rotate motions laid out along the shortest path to the target, our Short-Term Aborting A* (STAA*) converges fast and can be aborted early in order to guarantee a high and steady control rate. While our STAA* expands states along the shortest path, it takes care of collision checking with the environment including predicted states of moving obstacles, and returns the best solution found when the computation time runs out. Despite the bounded computation time, our STAA* does not get trapped in corners due to the following of the shortest path. In simulated and real-robot experiments, we demonstrate that our control approach eliminates collisions almost entirely and is superior to an improved version of the Dynamic Window Approach with predictive collision avoidance capabilities [1].

The segmentation of a point cloud into planar primitives is a popular approach to first-line scene interpretation and is particularly useful in mobile robotics for the extraction of drivable or walkable surfaces and for tabletop segmentation for manipulation purposes. Unfortunately, the planar segmentation task becomes particularly challenging when the point clouds are obtained from an inherently noisy, robot-mounted sensor that is often in motion, therefor requiring real time processing capabilities. We present a real time-capable plane segmentation technique based on a region growing algorithm that exploits the organized structure of point clouds obtained from RGB-D sensors. In order to counteract the sensor noise, we invest into careful selection of seeds that start the region growing and avoid the computation of surface normals whenever possible. We implemented our algorithm in C++ and thoroughly tested it in both simulated and real-world environments where we are able to compare our approach against existing state-of-the-art methods implemented in the Point Cloud Library. The experiments presented here suggest that our approach is accurate and fast, even in the presence of considerable sensor noise.

The detection of planar surfaces in a point cloud is a popular technique for the extraction of drivable or walkable surfaces and for tabletop segmentation. Unfortunately, RGB-D sensors are quite noisy and provide incomplete data, which makes the extraction of surfaces more challenging. Also, it is desirable to process the point cloud data in real time, which at a rate of approximately 30 Hz, leaves only a small amount of computation time per frame. We have already developed a real time-capable plane segmentation method [1] that exploits the organized structure of RGB-D point clouds in order to implement a computationally efficient region growing algorithm. It uses the point-plane distance to assign points to their segments rather than inherently unreliable surface normals. Now we are presenting an improvement where we adapt thresholds and other parameters of our algorithm to the measured depth in order to account for an increasing scatter of the points at larger distances from the camera. We estimate a minimum detectable plane size in pixels dependent on the measured depth. This enables us to stride in pixel coordinates with larger steps that are adaptive to the measured depth and to implement more robust sanity checks of depth-dependent size. Apart from a speed-up of the runtime of our algorithm, the segmentation quality also increased. We show a comparison between our improvement, our previous version, and other state-of-the-art methods evaluated on multiple commonly available datasets.

Geometric primitives are a compact and versatile representation of the environment and the objects within. From a motion planning perspective, the geometric structure can be leveraged in order to implement potentially faster and smoother motion control algorithms than it has been possible with grid-based occupancy maps so far. In this paper, we introduce a novel perception pipeline that efficiently processes the point cloud obtained from an RGB-D sensor in order to produce a floor-projected 2D map in the field-of-view of the robot where obstacles are represented as polygons rather than cells. These polygons can then be processed by path planning algorithms and obstacle avoidance controllers. Our pipeline includes a ground floor plane detector that performs significantly faster than other contemporary solutions and a grid segmentation algorithm that uses image processing techniques to identify the contours of obstacles in order to convert them to polygons. We demonstrate the performance of our approach in experiments with a wheeled and a humanoid robot and show that our polygonal perception pipeline works robustly even in the presence of the disturbances caused by the shaking of a walking robot.