Adaptive computational SLAM incorporating strategies of exploration and path planning | The Knowledge Engineering Review | Cambridge Core (original) (raw)

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.

References

Baltes, J., Tu, K. Y., Sadeghnejad, S. & Anderson, J. 2017. HuroCup: competition for multi-event humanoid robot athletes. Knowledge Engineering Review 32, e1.CrossRefGoogle Scholar

Bennewitz, M., Burgard, W. & Thrun, S. 2002. Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots. Robotics and Autonomous Systems 41, 89–99.CrossRefGoogle Scholar

Chatterjee, A. 2009. Differential evolution tuned fuzzy supervisor adapted, extended Kalman filtering for SLAM problems in mobile robots. Journal of Robotica 27, 411–423.CrossRefGoogle Scholar

Chatterjee, A. & Matsuno, F. 2007. A neuro-fuzzy assisted extended Kalman filter based approach for simultaneous localization and mapping (SLAM) problems. IEEE Trans. on Fuzzy Systems 15, 984–997.CrossRefGoogle Scholar

Chatterjee, A. & Matsuno, F. 2010. A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots. Expert Systems with Applications 37, 5542–5548.CrossRefGoogle Scholar

Dissanayake, G., Williams, S. B., Durrant-Whyte, H. F. & Bailey, T. 2002. Map management for efficient simultaneous localization and mapping (SLAM). Journal of Autonomous Robots 12, 267–286.CrossRefGoogle Scholar

Hsu, C. C., Wang, W. Y., Lin, T. Y., Wang, Y. T. & Huang, T. W. 2017. Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots. International Journal of Humanoid Robotics, 14, 1750007-1-17.CrossRefGoogle Scholar

Hsu, C. C., Yang, C. K., Wang, Y. T., Wang, W. Y. & Chien, C. H. 2017. Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots. Engineering Computations 34, 1217–1239.CrossRefGoogle Scholar

Keidar, M. & Kaminka, G. 2012. Robot exploration with fast frontier detection: theory and experiments. In Proc. of AAMAS, Valencia, 113–120.Google Scholar

Montemerlo, M., Thrun, S., Koller, D. & Wegbreit, B. 2002. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In Proceedings of AAAI National Conference on Artificial Intelligence, 593–598.Google Scholar

Montemerlo, M., Thrun, S., Koller, D. & Wegbreit, B. 2003. FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In Proc. of the 16th Int. Joint Conf. on Artificial Intelligence, 1151–1156.Google Scholar

Murphy, K. 2000. Bayesian map learning in dynamic environments. Neural Information Proceedings System 12, 1015–1021.Google Scholar

Smith, R. C. & Cheeseman, P. 1986. On the representation and estimation of spatial uncertainty. International Journal of Robotics 5, 56–58.CrossRefGoogle Scholar

Tang, L., Dian, S., Gu, G., Zhou, K., Wang, S. & Feng, X. 2010. A novel potential field method for obstacle avoidance and path planning of mobile robot. In Proc. of ICCSIT, Chengdu, 633–637.Google Scholar

Uslu, E., Cakmak, F., Balcilar, M., Akinci, A., Amasyali, M. F. & Yavuz, S. 2015. Implementation of frontier-based exploration algorithm for an autonomous robot. In Proc. of INISTA, Madrid, 1–7.Google Scholar

Williams, S. B., Dissanayake, G. & Durrant-Whyte, H. F. 2002. An efficient approach to the simultaneous localization and mapping problem. In Proc. of the IEEE International Conference on Robotics and Automation, Washington, USA, 406–411.Google Scholar

Yamauchi, B. 1997. A frontier-based approach for Autonomous exploration. In Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, CA, 146–151.Google Scholar

Yang, C. K., Hsu, C. C. & Wang, Y. T. 2013. Computationally efficient algorithm for simultaneous localization and mapping (SLAM). In Proc. of the IEEE Int. Conf. Networking, Sensing and Control, 328–332.Google Scholar