Obstacle Detection in Foliage with Ladar and Radar

Abstract

Autonomous off-road navigation is central to several important applications of unmanned ground vehicles. This requires the ability to detect obstacles in vegetation. We examine the prospects for doing so with scanning ladar and with a linear array of 2.2 GHz micro-impulse radar transceivers. For ladar, we summarize our work to date on algorithms for detecting obstacles in tall grass with single-axis ladar, then present a simple probabilistic model of the distance into tall grass that ladar-based obstacle detection is possible. This model indicates that the ladar “penetration depth” can range from on the order of 10 cm to several meters, depending on the plant type. We also present an experimental investigation of mixed pixel phenomenology for a time-of-flight, SICK ladar and discuss briefly how this bears on the problem. For radar, we show results of applying an existing algorithm for multi-frequency diffraction tomography to a set of 45 scans taken with one sensor translating laterally 4 cm/scan to mimic a linear array of transceivers. This produces a high resolution, 2-D map of scattering surfaces in front of the array and clearly reveals a large tree trunk behind over 2.5 m of thick foliage. Both types of sensor warrant further development and exploitation for this problem.