Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud
Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data....
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9548034/ |
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| author | Victor Jimenez Jorge Godoy Antonio Artunedo Jorge Villagra |
| author_facet | Victor Jimenez Jorge Godoy Antonio Artunedo Jorge Villagra |
| author_sort | Victor Jimenez |
| collection | DOAJ |
| description | Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes’s public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations. |
| format | Article |
| id | doaj-art-8dbf635cd0944b3588cf56ea5aaf5996 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-8dbf635cd0944b3588cf56ea5aaf59962025-08-20T02:03:34ZengIEEEIEEE Access2169-35362021-01-01913291413292710.1109/ACCESS.2021.31156649548034Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point CloudVictor Jimenez0https://orcid.org/0000-0003-1197-0937Jorge Godoy1https://orcid.org/0000-0002-3132-5348Antonio Artunedo2https://orcid.org/0000-0003-2161-9876Jorge Villagra3https://orcid.org/0000-0002-3963-7952Centre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainCentre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainCentre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainCentre for Automation and Robotics (CSIC-UPM), Arganda del Rey, SpainDistinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes’s public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations.https://ieeexplore.ieee.org/document/9548034/Belief propagationchannel-basedgeometric featuresLiDARMarkov random fieldobstacle-ground segmentation |
| spellingShingle | Victor Jimenez Jorge Godoy Antonio Artunedo Jorge Villagra Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud IEEE Access Belief propagation channel-based geometric features LiDAR Markov random field obstacle-ground segmentation |
| title | Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud |
| title_full | Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud |
| title_fullStr | Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud |
| title_full_unstemmed | Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud |
| title_short | Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud |
| title_sort | ground segmentation algorithm for sloped terrain and sparse lidar point cloud |
| topic | Belief propagation channel-based geometric features LiDAR Markov random field obstacle-ground segmentation |
| url | https://ieeexplore.ieee.org/document/9548034/ |
| work_keys_str_mv | AT victorjimenez groundsegmentationalgorithmforslopedterrainandsparselidarpointcloud AT jorgegodoy groundsegmentationalgorithmforslopedterrainandsparselidarpointcloud AT antonioartunedo groundsegmentationalgorithmforslopedterrainandsparselidarpointcloud AT jorgevillagra groundsegmentationalgorithmforslopedterrainandsparselidarpointcloud |