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Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots.

Pavel Gonzalez1, Alicia Mora1, Santiago Garrido1

  • 1Robotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, Spain.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces multi-LiDAR sensor fusion for enhanced mobile robot mapping and navigation. Combining 2D and 3D data improves environmental perception and enables advanced features like room segmentation.

Keywords:
Harmony SearchLiDAR odometrySLAMscan matchingscene segmentationtopological

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Current mobile robot applications primarily use 2D LiDAR for indoor tasks.
  • Single data type mapping is insufficient for six-degree-of-freedom environments.
  • Multi-LiDAR sensor fusion enhances robot mapping capabilities by integrating diverse data types.

Purpose of the Study:

  • To develop advanced techniques for mapping and navigation in indoor environments using Multi-LiDAR sensor fusion.
  • To improve robot perception and overcome limitations of single-sensor systems.
  • To enable robust indoor localization and environmental understanding.

Main Methods:

  • Implemented an Iterative Closest Point (ICP) scan matching algorithm with a distance threshold association counter as a multi-objective fitness function.
  • Utilized Harmony Search for optimizing scan matching results without initial guesses or odometry.
  • Developed a global Simultaneous Localization and Mapping (SLAM) approach to minimize accumulated errors.
  • Integrated 2D and 3D mapping techniques, overlapping resulting maps for fused geometrical information at different heights.
  • Proposed a room segmentation procedure analyzing fused geometrical data to overcome 2D map occlusions.

Main Results:

  • Achieved superior mapping and navigation performance compared to solo odometry LiDAR matching.
  • Successfully built global maps during SLAM, reducing accumulated errors.
  • Demonstrated effective fusion of geometrical information from 2D and 3D maps.
  • Validated the room segmentation procedure by implementing a successful door recognition system.
  • Confirmed algorithm performance in both simulated and real-world experimental scenarios.

Conclusions:

  • Multi-LiDAR sensor fusion significantly enhances indoor mobile robot mapping and navigation capabilities.
  • The proposed ICP-based scan matching with Harmony Search optimization provides robust localization.
  • Fused 2D and 3D mapping effectively addresses occlusions and improves environmental understanding.
  • The developed room segmentation and door recognition systems showcase the practical benefits of the fusion approach.