Multi robot fastSLAM

Balage, Dilhan (2010) Multi robot fastSLAM. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (5MB)

Abstract

Robotic mapping has been an active research area in robotics for last two decades. An accurate map is a mandatory requirement for a robot to work autonomously. In addition the robot requires to know its position with respect to a given map and this is solved through robot localization. The problem of solving both map building and robot localization is addressed by simultaneous localization and mapping (SLAM). A large volume of literature is available to solve the SLAM problem using a single robot. A robot will take a series of sensor readings about an unexplored area and then continues to build the map while knowing its position reference to partially built map. However, when an area becomes larger multi-robot SLAM is more efficient and also has the advantage of sharing the computational burden among several robots. Solving SLAM problem using multiple robot is important when there is large terrain to map and perhaps it will beyond the capability of single robot. Even if it is within the capability of single robot, such a deployment will not be cost and time effective. Therefore this research focuses on developing a multi-robot SLAM filter based on Fast SLAM algorithm. -- Single Pioneer 3AT robot was deployed to collect odometry and sensor readings. Grid based fastSLAM algorithm is implemented on MATLAB program code for offline processing and successfully generated the map of the environment. The data set obtained from single robot was divided into two data sets and they were treated as if they were obtained from two different robots. Single robot grid based fastSLAM algorithm was applied to both of the data sets and obtained two maps. Two maps were merged using Hough transform based map merging technique. Maps obtained from single robot SLAM and multi-robot SLAM is compared and multi-robot SLAM algorithm provides maps as same accuracy as single robot SLAM.

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