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3 May 2016 Improving the detection of explosive hazards with LIDAR-based ground plane estimation
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Three-dimensional point clouds generated by LIDAR offer the potential to build a more complete understanding of the environment in front of a moving vehicle. In particular, LIDAR data facilitates the development of a non-parametric ground plane model that can filter target predictions from other sensors into above-ground and below-ground sets. This allows for improved detection performance when, for example, a system designed to locate above-ground targets considers only the set of above-ground predictions. In this paper, we apply LIDAR-based ground plane filtering to a forward looking ground penetrating radar (FLGPR) sensor system and a side looking synthetic aperture acoustic (SAA) sensor system designed to detect explosive hazards along the side of a road. Additionally, we consider the value of the visual magnitude of the LIDAR return as a feature for identifying anomalies. The predictions from these sensors are evaluated independently with and without ground plane filtering and then fused to produce a combined prediction confidence. Sensor fusion is accomplished by interpolating the confidence scores of each sensor along the ground plane model to create a combined confidence vector at specified points in the environment. The methods are tested along an unpaved desert road at an arid U.S. Army test site.
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A. Buck, J. M. Keller, and M. Popescu "Improving the detection of explosive hazards with LIDAR-based ground plane estimation", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 982313 (3 May 2016);

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