ABSTRACT
Radar offers advantages as a robotic perception modality because it is not as vulnerable to vacuum, dust, fog, rain, snow and light conditions found in construction, mining, agricultural and planetary–exploration environments. Radar has significant echo signal-to-noise variations between observations as well as large footprint, side lobes, specularity effects and limited range resolution-all of which result in poor environment maps. Evidence grids are flexible and powerful probabilistic method for fusing multiple sensor observations.
This paper presents the development of a radar sensor model, which can fuse amplitude-vector sensor data into an evidence grid. A study of radar phenomena and FMCW signal processing suggests rules for signal interpretation. The sensor model uses these interpretation rules and captures the volumetric beam geometry. The results include a three dimensional map of an out door scene. This work is a step towards building high fidelity maps to be used in mobile robot navigation, obstacle avoidance and tool deployment under all visibility conditions.
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