Publication: Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots
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Publication date
2022-05
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Publisher
MDPI AG
Abstract
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.
Description
Keywords
Lidar odometry, Scan matching, Slam, Scene segmentation, Topological, Harmony search
Bibliographic citation
Gonzalez, P., Mora, A., Garrido, S., Barber, R., & Moreno, L. (2022). Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots. In Sensors, 22, (10), 3690-3710