Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots
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 level...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/10/3690 |
| _version_ | 1797495778541830144 |
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| author | Pavel Gonzalez Alicia Mora Santiago Garrido Ramon Barber Luis Moreno |
| author_facet | Pavel Gonzalez Alicia Mora Santiago Garrido Ramon Barber Luis Moreno |
| author_sort | Pavel Gonzalez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:54:31Z |
| format | Article |
| id | doaj.art-edd36ba4f22c46229e6e378ebdf22491 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:54:31Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-edd36ba4f22c46229e6e378ebdf224912023-11-23T12:59:30ZengMDPI AGSensors1424-82202022-05-012210369010.3390/s22103690Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile RobotsPavel Gonzalez0Alicia Mora1Santiago Garrido2Ramon Barber3Luis Moreno4Robotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, SpainRobotics Lab, Universidad Carlos III de Madrid, 28911 Leganes, SpainNowadays, 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.https://www.mdpi.com/1424-8220/22/10/3690LiDAR odometryscan matchingSLAMscene segmentationtopologicalHarmony Search |
| spellingShingle | Pavel Gonzalez Alicia Mora Santiago Garrido Ramon Barber Luis Moreno Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots Sensors LiDAR odometry scan matching SLAM scene segmentation topological Harmony Search |
| title | Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots |
| title_full | Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots |
| title_fullStr | Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots |
| title_full_unstemmed | Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots |
| title_short | Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots |
| title_sort | multi lidar mapping for scene segmentation in indoor environments for mobile robots |
| topic | LiDAR odometry scan matching SLAM scene segmentation topological Harmony Search |
| url | https://www.mdpi.com/1424-8220/22/10/3690 |
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