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A two-tier map representation for compact-stereo-vision-based SLAM

Published online by Cambridge University Press:  14 June 2011

D. C. Herath ,
S. Kodagoda  and
G. Dissanayake
D. C. Herath*
Affiliation:
Thinking Head Project, MARCS Auditory Laboratories, University of Western Sydney, Australia.
S. Kodagoda
Affiliation:
ARC Centre of Excellence for Autonomous Systems, University of Technology, Sydney, Australia. E-mails: s.kodagoda@cas.edu.au, g.dissanayake@cas.edu.au
G. Dissanayake
Affiliation:
ARC Centre of Excellence for Autonomous Systems, University of Technology, Sydney, Australia. E-mails: s.kodagoda@cas.edu.au, g.dissanayake@cas.edu.au
*
*Corresponding author. E-mail: damithc@gmail.com
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Summary

Vision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm.

Keywords

SLAMComputer visionNavigationRobot localizationPose estimation and registration
Type
Articles
Information
Robotica , Volume 30 , Issue 2 , March 2012 , pp. 245 - 256
Copyright
Copyright © Cambridge University Press 2011

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