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Proceedings Paper

Reduction of the lateral localization error of targets moving through a LIDAR beam
Author(s): Konrad Wenzl; Heinrich Ruser; Christian Kargel
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Paper Abstract

To track walking persons inside a surveillance area we use LIDAR (LIght Detection And Ranging) sensors with a small number N of spatially stationary LIDAR beams in order to keep the sensor costs to a minimum. To achieve high target detectability and tracking performance, the coverage of the surveillance area by the N LIDAR beams must be large, which is why the beamwidth is to be set to a practically feasible maximum. As a result, the lateral localization error inside these wide LIDAR beams is high while the area of surveillance can still not be entirely covered by LIDAR beams. Thus, the accurate tracking of persons walking inside the area of surveillance is challenging. In the classical tracking approach, the axial position of a target inside a LIDAR beam is obtained from time-of- ight measurements. However, the lateral deviation of the target position from the optical beam axis remains unknown. In this paper, a novel approach to reduce the lateral localization error is proposed and investigated. From consecutively measured (axial) distances to the target while it moves through a LIDAR beam the target velocity vector is estimated and used as observation for a Kalman-based tracking algorithm. The localization and tracking performances of the novel approach are determined and compared with those of the classical approach.

Paper Details

Date Published: 24 May 2013
PDF: 12 pages
Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87911R (24 May 2013); doi: 10.1117/12.2020528
Show Author Affiliations
Konrad Wenzl, Univ. der Bundeswehr München (Germany)
Heinrich Ruser, Univ. der Bundeswehr München (Germany)
Christian Kargel, Univ. der Bundeswehr München (Germany)


Published in SPIE Proceedings Vol. 8791:
Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection
Fabio Remondino; Jürgen Beyerer; Fernando Puente León; Mark R. Shortis, Editor(s)

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