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Adaptive computational SLAM incorporating strategies of exploration and path planning

Part of: Robot Athletes and Entertainers

Published online by Cambridge University Press:  02 December 2019

Jacky Baltes Open the ORCID record for Jacky Baltes [Opens in a new window] ,
Da-Wei Kung ,
Wei-Yen Wang  and
Chen-Chien Hsu
Jacky Baltes
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan e-mail: jhsu@ntnu.edu.tw
Da-Wei Kung
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan e-mail: jhsu@ntnu.edu.tw
Wei-Yen Wang
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan e-mail: jhsu@ntnu.edu.tw
Chen-Chien Hsu
Affiliation:
Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan e-mail: jhsu@ntnu.edu.tw
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Abstract

Simultaneous localization and mapping (SLAM) is a well-known and fundamental topic for autonomous robot navigation. Existing solutions include the FastSLAM family-based approaches which are based on Rao–Blackwellized particle filter. The FastSLAM methods slow down greatly when the number of landmarks becomes large. Furthermore, the FastSLAM methods use a fixed number of particles, which may result in either not enough algorithms to find a solution in complex domains or too many particles and hence wasted computation for simple domains. These issues result in reduced performance of the FastSLAM algorithms, especially on embedded devices with limited computational capabilities, such as commonly used on mobile robots. To ease the computational burden, this paper proposes a modified version of FastSLAM called Adaptive Computation SLAM (ACSLAM), where particles are predicted only by odometry readings, and are updated only when an expected measurement has a maximum likelihood. As for the states of landmarks, they are also updated by the maximum likelihood. Furthermore, ACSLAM uses the effective sample size (ESS) to adapt the number of particles for the next generation. Experimental results demonstrated that the proposed ACSLAM performed 40% faster than FastSLAM 2.0 and also has higher accuracy.

Information

Type
Robot Athletes and Entertainers
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e23
Copyright
© Cambridge University Press 2019 

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