This paper presents a system for egomotion
estimation using a stereo head camera. The
camera motion estimation is based on features
tracked along a video sequence. The system
also estimates the tridimensional geometry of
the environment by fusing the visual information from multiple views. Furthermore, the paper presents comparisons between two different algorithms. The first one is by applying triangulation to 3D points. Motion estimation using 3D points suffers from the problem of nonisotropic
noise due to the large uncertainty in
depth estimation. To deal with this problem
we present results with a second approach that works directly in the disparity space. Experimental results using a mobile platform are presented. The experiments cover long distances in urban-like environments with the presence of dynamic objects. The system presented is part of a bigger project involving autonomous navigation using vision only.
CitationHernández, A. [et al.]. Large scale visual odometry using stereo vision. A: Australasian Conference on Robotics and Automation. "Australasian Conference on Robotics and Automation". Sydney: 2009, p. 1-7.
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