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Proceedings Paper

A pose estimation method for unmanned ground vehicles in GPS denied environments
Author(s): Amirhossein Tamjidi; Cang Ye
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Paper Abstract

This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ~1.9% of the path length.

Paper Details

Date Published: 25 May 2012
PDF: 12 pages
Proc. SPIE 8387, Unmanned Systems Technology XIV, 83871K (25 May 2012); doi: 10.1117/12.920832
Show Author Affiliations
Amirhossein Tamjidi, Univ. of Arkansas at Little Rock (United States)
Cang Ye, Univ. of Arkansas at Little Rock (United States)

Published in SPIE Proceedings Vol. 8387:
Unmanned Systems Technology XIV
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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