In recent years, depth sensors have become more and more affordable and have found their way into a growing amount of robotic systems. However, mono- or multi-modal sensor registration, often a necessary step for further processing, faces many challenges on raw depth images or point clouds. This paper presents a method of converting depth data into images capable of visualizing spatial details that are basically hidden in traditional depth images. After noise removal, a neighborhood of points forms two normal vectors whose difference is encoded into this new conversion. Compared to Bearing Angle images, our method yields brighter, higher-contrast images with more visible contours and more details. We tested feature-based pose estimation of both conversions in a visual odometry task and RGB-D SLAM. For all tested features, AKAZE, ORB, SIFT, and SURF, our new Flexion images yield better results than Bearing Angle images and show great potential to bridge the gap between depth data and classical computer vision. Source code is available here: https://rlsch.github.io/depth-flexion-conversion.

Underground mines are a dangerous working environment and, therefore, robots could help putting less humans at risk. Traditional robots, sensors, and software often do not work reliably underground due to the harsh environment. This paper analyzes requirements and presents a robot design capable of navigating autonomously underground and manipulating objects with a robotic arm. The robot's base is a robust four wheeled platform powered by electric motors and able to withstand the harsh environment. It is equipped with color and depth cameras, lighting, laser scanners, an inertial measurement unit, and a robotic arm. We conducted two experiments testing mapping and autonomous navigation. Mapping a 75 meters long route including a loop closure results in a map that qualitatively matches the original map to a good extent. Testing autonomous driving on a previously created map of a second, straight, 150 meters long route was also successful. However, without loop closure, rotation errors cause apparent deviations in the created map. These first experiments showed the robot's operability underground.

A frequently raised argument against safelydriving automated vehicles is that they would not harmonize well with traffic flow-unrealistically large headways would invite other traffic participants to cut in and thus put passengers of following automated vehicles at risk.In order to test this hypothesis, we use real data of thousands of vehicles recorded in the United States as part of the Next Generation Simulation (NGSIM) program.To study the hypothesis, we pretend each human-driven vehicle is automated: These automated vehicles drive exactly as the recorded human drivers, but they have a much smaller reaction time and thus can still drive safely in situations that are unsafe for human drivers.The main result is that in only very few cases an automated vehicle would not drive safely, although more than half of the human drivers do not keep a safe distance according to their capabilities.Thus, there is no increased risk of vehicles cutting in-and if they do, automated vehicles are only at risk in around 10% of all cases compared to around 60% for human drivers.