Improving data association in vision-based SLAM

Gil, Arturo and Reinoso, Oscar and Burgard, Wolfram and Stachniss, Cyrill and Martinez Mozos, Oscar (2006) Improving data association in vision-based SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9-15 October 2006, Beijing, China.

Full content URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...

Documents
gil2006iros.pdf
[img] PDF
gil2006iros.pdf
Restricted to Repository staff only

7MB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem for autonomous mobile robots using visual landmarks. Our map is represented by a set of three dimensional landmarks referred to a global reference frame. We use significant points extracted from stereo images as natural landmarks, in particular we employ SIFT features found in the environment. Each landmark contains a visual descriptor that partially differentiates it from others. Our method is based on a Rao-Blackwellized particle filter, thus the problem is decomposed into two parts: one estimation over robot paths, and N independent estimations over landmark positions, each conditioned on the path estimate. We actively track visual landmarks at a local neighbourhood and select only those that are more stable. When a visual feature has been observed from a significant number of frames it is then integrated in the filter. By this procedure, the total number of landmarks in the map is reduced, compared to prior approaches. Due to the tracking of each landmark, we obtain different examples that represent the same natural landmark. We use this fact to improve data association. Finally, efficient resampling techniques have been applied, which reduces the number of particles needed and avoids the particle depletion problem.

Keywords:SLAM, mobile robots, particle filtering, robot vision, stereo image processing
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:9582
Deposited On:22 May 2013 15:55

Repository Staff Only: item control page