Probabilistic voxel mapping using an adaptive confidence measure of stereo matching

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 440
  • Download : 107
In this paper, we describe a probabilistic voxel mapping algorithm using an adaptive confidence measure of stereo matching. Most of the 3D mapping algorithms based on stereo matching usually generate a map formed by point cloud. There are many reconstruction errors. The reconstruction errors are due to stereo reconstruction error factors such as calibration errors, stereo matching errors, and triangulation errors. A point cloud map with reconstruction errors cannot accurately represent structures of environments and needs large memory capacity. To solve these problems, we focused on the confidence of stereo matching and probabilistic representation. For evaluation of stereo matching, we propose an adaptive confidence measure that is suitable for outdoor environments. The confidence of stereo matching can be reflected in the probability of restoring structures. For probabilistic representation, we propose a probabilistic voxel mapping algorithm. The proposed probabilistic voxel map is a more reliable representation of environments than the commonly used voxel map that just contains the occupancy information. We test the proposed confidence measure and probabilistic voxel mapping algorithm in outdoor environments. © 2012 Springer-Verlag Berlin Heidelberg.
Publisher
Springer Verlag
Issue Date
2013-04
Language
English
Citation

Intelligent Service Robotics, v.6, no.2, pp.89 - 99

ISSN
1861-2776
DOI
10.1007/s11370-012-0125-z
URI
http://hdl.handle.net/10203/103030
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0