DocumentCode :
2603176
Title :
Fast randomized planner for SLAM automation
Author :
Parulkar, Amey ; Shukla, Piyush ; Krishna, K. Madhava
Author_Institution :
Robot. Res. Center, IIIT Hyderabad, Hyderabad, India
fYear :
2012
fDate :
20-24 Aug. 2012
Firstpage :
765
Lastpage :
770
Abstract :
In this paper, we automate the traditional problem of Simultaneous Localization and Mapping (SLAM) by interleaving planning for exploring unknown environments by a mobile robot. We denote such planned SLAM systems as SPLAM (Simultaneous Planning Localization and Mapping). The main aim of SPLAM is to plan paths for the SLAM process such that the robot and map uncertainty upon execution of the path remains minimum and tractable. The planning is interleaved with SLAM and hence the terminology SPLAM. While typical SPLAM routines find paths when the robot traverses amidst known regions of the constructed map, herein we use the SPLAM formulation for an exploration like situation. Exploration is carried out through a frontier based approach where we identify multiple frontiers in the known map. Using Randomized Planning techniques we calculate various possible trajectories to all the known frontiers. We introduce a novel strategy for selecting frontiers which mimics Fast SLAM, selects a trajectory for robot motion that will minimize the map and robot state covariance. By using a Fast SLAM like approach for selecting frontiers we are able to decouple the robot and landmark covariance resulting in a faster selection of next best location, while maintaining the same kind of robustness of an EKF based SPLAM framework. We then compare our results with Shortest Path Algorithm and EKF based Planning. We show significant reduction in covariance when compared with shortest frontier first approach, while the uncertainties are comparable to EKF-SPLAM albeit at much faster planning times.
Keywords :
Kalman filters; SLAM (robots); covariance analysis; mobile robots; path planning; random processes; EKF; SLAM automation; SPLAM; frontier based approach; interleaving planning; landmark covariance; map uncertainty; mobile robot; path planning; randomized planning technique; robot state covariance; robot trajectory; shortest path algorithm; simultaneous planning localization and mapping; Covariance matrix; Planning; Simultaneous localization and mapping; Trajectory; Uncertainty; Exploration; FastSLAM; SPLAM; trajectory Planning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation Science and Engineering (CASE), 2012 IEEE International Conference on
Conference_Location :
Seoul
ISSN :
2161-8070
Print_ISBN :
978-1-4673-0429-0
Type :
conf
DOI :
10.1109/CoASE.2012.6386480
Filename :
6386480
Link To Document :
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