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Topological Based Scan Matching – Odometry Posterior Sampling in RBPF Under Kinematic Model Failures

Abstract

Rao-Blackwellized Particle Filters (RBPF) have been utilized to provide a solution to the SLAM problem. One of the main factors that cause RBPF failure is the potential particle impoverishment. Another popular approach to the SLAM problem are Scan Matching methods, whose good results require environments with lots of information, however fail in the lack thereof. To face these issues, in the current work techniques are presented to combine Rao-Blackwellized particle filters with a scan matching algorithm (CRSM SLAM). The particle filter maintains the correct hypothesis in environments lacking features and CRSM is employed in feature-rich environments while simultaneously reduces the particle filter dispersion. Since CRSM’s good performance is based on its high iteration frequency, a multi-threaded combination is presented which allows CRSM to operate while RBPF updates its particles. Additionally, a novel method utilizing topological information is proposed, in order to reduce the number of particle filter resamplings. Finally, we present methods to address anomalous situations where scan matching can not be performed and the vehicle displays behaviors not modeled by the kinematic model, causing the whole method to collapse. Numerous experiments are conducted to support the aforementioned methods’ advantages.

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Correspondence to Aristeidis G. Thallas.

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Thallas, A.G., Tsardoulias, E.G. & Petrou, L. Topological Based Scan Matching – Odometry Posterior Sampling in RBPF Under Kinematic Model Failures. J Intell Robot Syst 91, 543–568 (2018). https://doi.org/10.1007/s10846-017-0730-3

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  • DOI: https://doi.org/10.1007/s10846-017-0730-3

Keywords

  • Autonomous robots
  • SLAM
  • Rao-blackwellized particle filter
  • Scan matching
  • CRSM
  • Occupancy grid map