Authors:
Hans Jørgen Andersen
;
Morten Friesgaard Christensen
and
Thomas Bak
Affiliation:
Aalborg University, Denmark
Keyword(s):
Computer vision, Autonomous mobile robot, Visual Odometry, GPS, Kalman filtering.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Visual Navigation
;
Visually Guided Robotics
Abstract:
Localization is an essential part of autonomous vehicles or robots navigating in an outdoor environment. In the absence of an ideal sensor for localization, it is necessary to use sensors in combination in order to achieve acceptable results. In the present study we present a combination of GPS and visual motion estimation, which have complementary strengths. The visual motion estimation is based on the tracking of points in an image sequence. In an open field outdoor environment the points being tracked are typically distributed in one dimension (on a line), which allows the ego motion to be determined by a new method based on simple analysis of the image point set covariance structure. Visual motion estimates are fused with GPS data in a Kalman filter. Since the filter tracks the state estimate over time, it is possible to use the prior estimate of the state to remove errors in the landmark matching, simplifying the matching, and increasing the robustness. The proposed algorithm is
evaluated against ground truth in a realistic outdoor experimental setup.
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