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Permanent link (DOI): https://doi.org/10.7939/R3FF3MB9S

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Theses and Dissertations

Fast-SeqSLAM: Place Recognition and Multi-robot Map Merging Open Access

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Other title
Subject/Keyword
Place recognition, SLAM, Loop closure detection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Siam, Sayem Mohammad
Supervisor and department
Zhang, Hong (Computing Science)
Examining committee member and department
Ray, Nilanjan (Computing Science)
Barcyzk, Martin (Mechanical Engineering)
Department
Department of Computing Science
Specialization

Date accepted
2017-01-04T13:44:11Z
Graduation date
2017-06:Spring 2017
Degree
Master of Science
Degree level
Master's
Abstract
Loop closure detection or place recognition is a fundamental problem in robot simultaneous localization and mapping (SLAM). SeqSLAM is considered to be one of the most successful algorithms for loop closure detection as it has been demonstrated to be able to handle significant environmental condition changes including those due to illumination, weather, and time of the day. However, SeqSLAM relies heavily on exhaustive sequence matching, a computationally expensive process that prevents the algorithm from being used in dealing with large maps. In this thesis, we propose Fast-SeqSLAM, an efficient version of SeqSLAM. Fast-SeqSLAM has a much reduced time complexity without degrading the accuracy, and this is achieved by (a) using an approximate nearest neighbor (ANN) algorithm to match the current image with those in the robot map and (b) extending the idea of SeqSLAM to greedily search a sequence of images that best match with the current sequence. We demonstrate the effectiveness of our Fast-SeqSLAM algorithm in two related applications: loop closure detection and integration of topological maps independently built by multiple robots operating in the same environment.
Language
English
DOI
doi:10.7939/R3FF3MB9S
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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