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Proceedings Paper

Improving the detection of explosive hazards with LIDAR-based ground plane estimation
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Paper Abstract

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.

Paper Details

Date Published: 3 May 2016
PDF: 13 pages
Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 982313 (3 May 2016); doi: 10.1117/12.2224101
Show Author Affiliations
A. Buck, Univ. of Missouri (United States)
J. M. Keller, Univ. of Missouri (United States)
M. Popescu, Univ. of Missouri (United States)


Published in SPIE Proceedings Vol. 9823:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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