Abstract
Abstract. Environmental perception is one of the core requirements in autonomous vehicle navigation. If exposed to harsh conditions, commonly deployed sensors like cameras or lidars deliver poor sensing performance. Millimeter wave radars enable robust sensing of the environment, but suffer from specular reflections and large beamwidths. To incorporate the sensor noise and lateral uncertainty, a new probabilistic, voxel-based recursive mapping method is presented to enable online terrain mapping using scanning radar sensors. For map accuracy evaluation, test measurements are performed with a scanning radar sensor in an off-road area. The voxel map is used to derive a digital terrain model, which can be compared with ground-truth data from an image-based photogrammetric reconstruction of the terrain. The method evaluation shows promising results for terrain mapping solely performed with radar scanners. However, small terrain structures still pose a problem due to larger beamwidths in comparison to lidar sensors.
Highlights
Over the last years, considerable progress was made in the broad research field of robotic navigation and mapping
An example for a generated voxel map from real radar measure ments is shown in Figure 4: on the top, occupied (red color, p > 0.5) and free (green color, p < 0.5) voxels are visualized, while on the bottom only occupied vox els are marked
The voxel state for map cell mi based on all measurements z1:t can be described as the occupancy probability p: p =
Summary
Considerable progress was made in the broad research field of robotic navigation and mapping. Cameras are versatile and cheap, but the quality of a stereo 3D reconstruction heavily decreases under non-ideal conditions, e.g. poor lighting or dirt on the lenses (Woodside Capital Partners, 2016) Under these conditions, such sensors are prone to erroneous measurements or require constant cleaning. Recent developments of scanning radar sensors enable environment measurements like typically known of lidar sensors, but with a more robust performance in harsh environments. On the downside, these radar sensors are quite inaccurate compared to a lidar scanner and prone to erroneous measurements due to their physical principle. In comparison to the first two sensor technologies, radars are not commonly used for terrain mapping tasks due to their lower mea- A robot-centric map was used which only displayed the camera’s field of view
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