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ICRA
1998
IEEE

Position Estimation Using Principal Components of Range Data

10 years 4 months ago
Position Estimation Using Principal Components of Range Data
1 sensors is to construct a structural description from sensor data and to match this description to a previously acquired model [Crowley 85]. An alternative is to project individual range measurements onto a previously acquired model [Leonard and Durrant-Whyte 91]. It is also possible to fuse range measurements directly using occupancy grids [Elfes 86], [Schiele 94]. Recently it has been shown that raw range data from nearby scans can be registered using a technique known as scan correlation [Weiss et al 95]. The correction vector from this technique provides a correction to position estimation. A thorough review in the state of the art in position estimation is provided in [Borenstein et al 96]. This paper describes a new 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 re...
James L. Crowley, Frank Wallner, Bernt Schiele
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1998
Where ICRA
Authors James L. Crowley, Frank Wallner, Bernt Schiele
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