A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM
Recent developments in LiDAR sensors make mobile mapping fast and cost effective. These sensors generate a large amount of data which in turn improves the coverage and details of the map. Due to the limited range of the sensor, one has to collect a series of scans to build the entire map of the en...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/273/2016/isprs-archives-XLI-B3-273-2016.pdf |
| _version_ | 1811237148761784320 |
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| author | A. Kurian K. W. Morin |
| author_facet | A. Kurian K. W. Morin |
| author_sort | A. Kurian |
| collection | DOAJ |
| description | Recent developments in LiDAR sensors make mobile mapping fast and cost effective. These sensors generate a large amount of data
which in turn improves the coverage and details of the map. Due to the limited range of the sensor, one has to collect a series of scans
to build the entire map of the environment. If we have good GNSS coverage, building a map is a well addressed problem. But in an
indoor environment, we have limited GNSS reception and an inertial solution, if available, can quickly diverge. In such situations,
simultaneous localization and mapping (SLAM) is used to generate a navigation solution and map concurrently. SLAM using point
clouds possesses a number of computational challenges even with modern hardware due to the shear amount of data. In this paper, we
propose two strategies for minimizing the cost of computation and storage when a 3D point cloud is used for navigation and real-time
map building. We have used the 3D point cloud generated by Leica Geosystems's Pegasus Backpack which is equipped with Velodyne
VLP-16 LiDARs scanners. To improve the speed of the conventional iterative closest point (ICP) algorithm, we propose a point cloud
sub-sampling strategy which does not throw away any key features and yet significantly reduces the number of points that needs to be
processed and stored. In order to speed up the correspondence finding step, a dual kd-tree and circular buffer architecture is proposed.
We have shown that the proposed method can run in real time and has excellent navigation accuracy characteristics. |
| first_indexed | 2024-04-12T12:21:00Z |
| format | Article |
| id | doaj.art-7c8c6f87d5db4c6da86ab4980e8a89e6 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T12:21:00Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-7c8c6f87d5db4c6da86ab4980e8a89e62022-12-22T03:33:18ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B327327810.5194/isprs-archives-XLI-B3-273-2016A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAMA. Kurian0K. W. Morin1Leica Geosystems Ltd., 245 Aero Way NE, Calgary, Alberta, Canada, T2E 6K2Leica Geosystems Ltd., 245 Aero Way NE, Calgary, Alberta, Canada, T2E 6K2Recent developments in LiDAR sensors make mobile mapping fast and cost effective. These sensors generate a large amount of data which in turn improves the coverage and details of the map. Due to the limited range of the sensor, one has to collect a series of scans to build the entire map of the environment. If we have good GNSS coverage, building a map is a well addressed problem. But in an indoor environment, we have limited GNSS reception and an inertial solution, if available, can quickly diverge. In such situations, simultaneous localization and mapping (SLAM) is used to generate a navigation solution and map concurrently. SLAM using point clouds possesses a number of computational challenges even with modern hardware due to the shear amount of data. In this paper, we propose two strategies for minimizing the cost of computation and storage when a 3D point cloud is used for navigation and real-time map building. We have used the 3D point cloud generated by Leica Geosystems's Pegasus Backpack which is equipped with Velodyne VLP-16 LiDARs scanners. To improve the speed of the conventional iterative closest point (ICP) algorithm, we propose a point cloud sub-sampling strategy which does not throw away any key features and yet significantly reduces the number of points that needs to be processed and stored. In order to speed up the correspondence finding step, a dual kd-tree and circular buffer architecture is proposed. We have shown that the proposed method can run in real time and has excellent navigation accuracy characteristics.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/273/2016/isprs-archives-XLI-B3-273-2016.pdf |
| spellingShingle | A. Kurian K. W. Morin A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM |
| title_full | A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM |
| title_fullStr | A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM |
| title_full_unstemmed | A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM |
| title_short | A FAST AND FLEXIBLE METHOD FOR META-MAP BUILDING FOR ICP BASED SLAM |
| title_sort | fast and flexible method for meta map building for icp based slam |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/273/2016/isprs-archives-XLI-B3-273-2016.pdf |
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