Title :
Distraction suppression for vision-based pose estimation at city scales
Author :
McManus, Colin ; Churchill, Winston ; Napier, Ashley ; Davis, Brian ; Newman, Paul
Author_Institution :
Mobile Robot. Group, Univ. of Oxford, Oxford, UK
Abstract :
This paper is concerned with the problem of egomotion estimation in highly dynamic, heavily cluttered urban environments over long periods of time. This is a challenging problem for vision-based systems because extreme scene movement caused by dynamic objects (e.g., enormous buses) can result in erroneous motion estimates. We describe two methods that combine 3D scene priors with vision sensors to generate background-likelihood images, which act as probability masks for objects that are not part of the scene prior. This results in a system that is able to cope with extreme scene motion, even when most of the image is obscured. We present results on real data collected in central London during rush hour and demonstrate the benefits of our techniques on a core navigation system - visual odometry.
Keywords :
feature extraction; motion estimation; pose estimation; probability; 3D scene priors; background-likelihood images; central London; city scales; core navigation system; distraction suppression; dynamic objects; egomotion estimation; extreme scene motion; extreme scene movement; heavily cluttered urban environments; probability masks; vision sensors; vision-based pose estimation; visual odometry; Detectors; Jacobian matrices; Laser modes; Navigation;
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
Print_ISBN :
978-1-4673-5641-1
DOI :
10.1109/ICRA.2013.6631106