A pose estimation method for unmanned ground vehicles in GPS denied environments

Amirhossein Tamjidi; Cang Ye

doi:10.1117/12.920832

25 May 2012 A pose estimation method for unmanned ground vehicles in GPS denied environments

Amirhossein Tamjidi, Cang Ye

Proceedings Volume 8387, Unmanned Systems Technology XIV; 83871K (2012) https://doi.org/10.1117/12.920832
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Amirhossein Tamjidi and Cang Ye "A pose estimation method for unmanned ground vehicles in GPS denied environments", Proc. SPIE 8387, Unmanned Systems Technology XIV, 83871K (25 May 2012); https://doi.org/10.1117/12.920832

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