Abstract
This paper proposes a novel method for estimating the positions of two vehicles in a global reference frame based on synchronized image and navigational information. The proposed technique leverages one vehicle's ability to localize itself relative to another using image data, enabling motion estimation from tracking common features. The visual odometry algorithm of this work uses the optimal vehicle motion over a single time interval estimated from the positions of common features in a bundle adjustment algorithm as a delayed state extended Kalman filter (EKF) measurement. The algorithm achieves accurate motion estimation and is a potential alternative to map-based simultaneous localization and mapping (SLAM) algorithms. Published 2015 This article is a U.S. Government work and is in the public domain in the USA.
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