Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

  • Authors:

  • Kyoungmin Lee;Se-Jin Lee;Mathias Kölsch;Wan Kyun Chung

  • Affiliations:

  • Department of Computer Science, Naval Postgraduate School, Monterey, USA;Kyungil University, Gyeongsan, Korea;Department of Computer Science, Naval Postgraduate School, Monterey, USA;Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea

  • Venue:
  • Autonomous Robots
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.