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2019 Volume 34
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ORIGINAL RESEARCH   Open Access    

Adaptive computational SLAM incorporating strategies of exploration and path planning

  • Department of Electrical Engineering

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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.
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  • Cite this article

    Jacky Baltes, Da-Wei Kung, Wei-Yen Wang, Chen-Chien Hsu. 2019. Adaptive computational SLAM incorporating strategies of exploration and path planning. The Knowledge Engineering Review. 34:183 doi: 10.1017/S0269888919000183
    Jacky Baltes, Da-Wei Kung, Wei-Yen Wang, Chen-Chien Hsu. 2019. Adaptive computational SLAM incorporating strategies of exploration and path planning. The Knowledge Engineering Review. 34:183 doi: 10.1017/S0269888919000183
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ORIGINAL RESEARCH   Open Access    

Adaptive computational SLAM incorporating strategies of exploration and path planning

  • Department of Electrical Engineering

  • Received: 20 April 2019
  • Revised: 28 October 2019
  • Accepted: 06 November 2019
The Knowledge Engineering Review  34,  Article number: e23  (2019)  |  Cite this article

Abstract: 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.

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    Acknowledgments

    • This work was financially supported by the ‘Chinese Language and Technology Center’ of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Science and Technology, Taiwan, under Grants No. MOST 108-2634-F-003-002, MOST 108-2634-F-003-003, and MOST 108-2634-F-003-004 (administered through Pervasive Artificial Intelligence Research (PAIR) Labs), as well as MOST 107-2811-E-003-503. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research.

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    Jacky Baltes, Da-Wei Kung, Wei-Yen Wang, Chen-Chien Hsu. 2019. Adaptive computational SLAM incorporating strategies of exploration and path planning. The Knowledge Engineering Review. 34:183 doi: 10.1017/S0269888919000183
    Jacky Baltes, Da-Wei Kung, Wei-Yen Wang, Chen-Chien Hsu. 2019. Adaptive computational SLAM incorporating strategies of exploration and path planning. The Knowledge Engineering Review. 34:183 doi: 10.1017/S0269888919000183
    shu

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