A minimalistic approach to appearance-based visual SLAM

Andreasson, Henrik and Duckett, Tom and Lilienthal, Achim (2008) A minimalistic approach to appearance-based visual SLAM. IEEE Transactions on Robotics, 24 (5). pp. 991-1001. ISSN 1552-3098

Documents
A minimalistic approach to appearance-based visual SLAM
Journal article in IEEE Transactions Robotics
[img]
[Download]
[img]
Preview
PDF
Andreasson_etal_2008-IEEE_TRO.pdf

1255Kb

Official URL: http://dx.doi.org/10.1109/TRO.2008.2004642

Abstract

This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a
relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple
view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses.

Item Type:Article
Keywords:mobile robotics, computer vision
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:2094
Deposited By: Tom Duckett
Deposited On:15 Dec 2009 22:19
Last Modified:18 Nov 2013 12:28

Repository Staff Only: item control page