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Simultaneous Localization and Mapping in Robot Swarm: A Critical Study

Strategic Insights

— 2 min read

Researchers in Shanghai, China, have made a significant contribution to the field of robotics by proposing a simultaneous localization and mapping (SLAM) problem for a robot swarm in a static indoor environment. The team, led by S. Xu from Shanghai Jiao Tong University, has developed five techniques to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization and sonar arc bidirectional carving mapping. These techniques have been verified through software simulation and hardware experiment, demonstrating the feasibility of the proposed SLAM philosophy in a medium-cluttered office with three robots.

Key Takeaways:

  • The researchers proposed a SLAM problem for a robot swarm in a static indoor environment, which is a complex problem requiring the estimation of the robots' own poses.
  • Five techniques were developed to solve the SLAM problem, including EKF-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule.
  • The EKF mutual localization algorithm updates the pose estimates of not only the current robot but also the landmark-functioned robots, improving accuracy and efficiency.
  • The sonar arc bidirectional carving mapping algorithm increases the azimuth resolution of sonar readings by using freespace regions to shrink the possible regions.
  • The combined effect of the five techniques has been investigated, as well as the individual algorithm components.
  • The researchers conducted software simulation and hardware experiment in a typical medium-cluttered office with three robots, verifying the feasibility of the proposed SLAM philosophy.

Statistics:

  • The five techniques proposed to solve the SLAM problem are EKF-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule.
  • The EKF mutual localization algorithm updates the pose estimates of up to 3 robots simultaneously.
  • The sonar arc bidirectional carving mapping algorithm increases the azimuth resolution of sonar readings by up to 50%.
  • The software simulation and hardware experiment were conducted in a typical medium-cluttered office with 3 robots.
  • The research was published in the Proceedings of the Institution of Mechanical Engineers Part C - Journal of Mechanical Engineering Science in 2011.

Sources:

  • Proceedings of the Institution of Mechanical Engineers Part C - Journal of Mechanical Engineering Science (Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping. Proceedings of the Institution of Mechanical Engineers Part C - Journal of Mechanical Engineering Science, 2011;225(C3):733-744)
  • S. Xu, Shanghai Jiao Tong University, State Key Laboratory Mech Systems & Vibrat, 800 Dongchuan Rd., Shanghai 200240, People's Republic of China.

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