Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots Articles uri icon

publication date

  • May 2022

start page

  • 3690

end page

  • 3710


  • 10


  • 22

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220


  • Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping,
    navigation, and low-level scene segmentation. However, single data type maps are not enough
    in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to
    map on different levels the surrounding environment. It exploits the benefits of several data types,
    counteracting the cons of each of the sensors. This research introduces several techniques to achieve
    mapping and navigation through indoor environments. First, a scan matching algorithm based on
    ICP with distance threshold association counter is used as a multi-objective-like fitness function.
    Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A
    global map is then built during SLAM, reducing the accumulated error and demonstrating better
    results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in
    2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different
    heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding
    occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition
    system. Experiments are conducted in both simulated and real scenarios, proving the performance of
    the proposed algorithms.


  • Robotics and Industrial Informatics


  • lidar odometry; scan matching; slam; scene segmentation; topological; harmony search