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Proceedings Paper

Robust self-calibration and evidential reasoning for building environment maps
Author(s): Arun P. Tirumalai; Brian G. Schunck; Ramesh C. Jain
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Paper Abstract

We address the problem of building a map of the environment utilizing sensory depth information obtained from multiple viewpoints. The desired representation of the environment is in the form of a finite-resolution three-dimensional grid of voxels. Each voxel within the grid is assigned a binary value corresponding to its occupancy state. We present an approach for multi-sensory depth information assimilation based on Dempster-Shafer theory for evidential reasoning. This approach provides a mechanism to explicitly model ignorance which is desirable when dealing with an unknown environment. A fundamental requirement for such an approach to be used is accurate knowledge of the camera motion between two viewpoints. We present a robust least median of squares (LMS) based algorithm to recover this motion which provides a self-calibration mechanism. We present results obtained from this approach on a laboratory stereo sequence.

Paper Details

Date Published: 1 April 1991
PDF: 14 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25270
Show Author Affiliations
Arun P. Tirumalai, Univ. of Michigan (United States)
Brian G. Schunck, Univ. of Michigan (United States)
Ramesh C. Jain, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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