Date of Original Version
Abstract or Description
Radar offers advantages as a robotic perception modality because it is not as vulnerable to the vacuum, dust, fog, rain, snow and light conditions found in construction, mining, agricultural and planetary-exploration environments. However radar has shortcomings such as a large footprint, sidelobes, specularity effects and limited range resolution—all of which result in poor environment maps. The fusion of successive radar observations can alleviate radar shortcomings and improve map fidelity. Sensor models exist for the fusion of sonar, laser and stereo into evidence grids. However radar outputs richer data and cannot use those existing models. This paper presents a sensor model that uses constant false alarm for occupancy detection and incorporates heuristic rules to approach occlusions. The resulting radar-based map of outdoors can suit robot obstacle avoidance, navigation and tool deployment. The limited map detail achieved suggests the need for a more rigorous probabilistic approach to encode the dependencies and estimate the model parameters.