Scene Modeling from Motion-Free Radar Sensing

Sonar, laser and stereo are common sensors used for safeguarding, mapping and navigation in robotic applications. However, vacuum, dust, fog, rain, snow and light conditions found in construction, mining, agricultural and planetary-exploration environments compromise the effectiveness of these sensors. Sonar can not operate in vacuum conditions, low visibility and scanning mechanisms limit laser’s reliability, and stereo is dependent on scene texture and illumination. Radar can offer remarkable advantages as a robotic perception mode because it is not as vulnerable to the aforementioned conditions. Also, radar can be electronically-steered. However, radar has shortcomings such as a large footprint, sidelobes, specularity effects and limited range resolution, all of which result in poor perception models. Some of these problems worsen with the introduction of motion-free scanning, which is important for high reliability and size reduction. Radar’s shortcomings provide the context for the proposed research. I plan to develop an interpreter for building high-resolution and high-fidelity perception models from motion-free scanning radar sensing. Quality maps will make radar an effective tool for numerous robotic applications.