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Position estimation using principal components of range data

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3 Author(s)
Crowley, J.L. ; INRIA, Monthbonnot Saint Martin, France ; Wallner, F. ; Schiele, B.

Describes an approach to mobile robot position estimation based on principal component analysis of laser range data. An eigenspace is constructed from the principal components of a large number of range data sets. The structure of an environment, as seen by a range sensor, is represented as a family of surfaces in this space. Subsequent range data sets from the environment project as a point in this space. Associating this point to the family of surfaces gives a set of candidate positions and orientations (poses) for the sensor. These candidate poses correspond to positions and orientations in the environment which have similar range profiles. A Kalman filter can used to select the most likely candidate pose based on coherence with small movements. The first part of this paper describes how a relatively small number of depth profiles of an environment can be used to generate a complete eigenspace. This space is used to build a representation of the range scan profiles obtained from a regular grid of positions and orientations (poses). This representation has the form of a family of surfaces (a manifold). This representation converts the problem of associating a range profile to possible positions and orientations into a table lookup. As a side benefit, the method provides a simple means to detect obstacles in a range profile. The final section of the paper reviews the use of estimation theory to determine the correct pose hypothesis by tracking

Published in:

Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on  (Volume:4 )

Date of Conference:

16-20 May 1998

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