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An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping.

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This study introduces a new Simultaneous Localization and Mapping (SLAM) algorithm for indoor mapping. The orthogonal feature weighted occupancy likelihood map (OWOLM) method improves accuracy and reduces errors in map construction.

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2D indoor mappingIMU-aided laser scan matchingSLAMorthogonal weighted occupancy likelihood map

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Area of Science:

  • Robotics
  • Computer Vision
  • Geographic Information Systems

Background:

  • Indoor mapping is crucial for location-based services.
  • Simultaneous Localization and Mapping (SLAM) is an efficient technique for creating indoor maps.
  • Existing SLAM methods can suffer from accumulated errors.

Purpose of the Study:

  • To propose a novel SLAM algorithm for 2D indoor mapping using laser scanner and IMU data.
  • To develop an enhanced occupancy likelihood map incorporating orthogonal features.
  • To improve the accuracy and clarity of indoor maps.

Main Methods:

  • A grid-based occupancy likelihood map is utilized.
  • Scan-to-map matching is employed to determine optimal transformations.
  • A lightweight orthogonal blurred segments (OBS) method is proposed to weight scan points.
  • The orthogonal feature weighted occupancy likelihood map (OWOLM) is constructed by fusing orthogonal features.

Main Results:

  • The OWOLM method effectively filters noise from objects like glass cabinets.
  • Experimental results in a library demonstrate reduced accumulated errors compared to general occupancy maps.
  • The proposed method constructs clearer and more accurate indoor maps.

Conclusions:

  • The OWOLM enhances indoor mapping by leveraging orthogonal environmental features.
  • This approach offers a robust solution for accurate 2D indoor map generation.
  • The method shows significant improvements in reducing mapping errors and enhancing map quality.