Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data
The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud a...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-07-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823003770 |
| _version_ | 1797795249742938112 |
|---|---|
| author | Zhenyu Liu Peter van Oosterom Jesús Balado Arjen Swart Bart Beers |
| author_facet | Zhenyu Liu Peter van Oosterom Jesús Balado Arjen Swart Bart Beers |
| author_sort | Zhenyu Liu |
| collection | DOAJ |
| description | The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption. |
| first_indexed | 2024-03-13T03:16:10Z |
| format | Article |
| id | doaj.art-7f3347d1ac7d4413b3c26214c26901b0 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-13T03:16:10Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-7f3347d1ac7d4413b3c26214c26901b02023-06-26T04:13:28ZengElsevierAlexandria Engineering Journal1110-01682023-07-0174327344Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning dataZhenyu Liu0Peter van Oosterom1Jesús Balado2Arjen Swart3Bart Beers4GIS Technology, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, the Netherlands; Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany; Corresponding author at: GIS Technology, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, the Netherlands.GIS Technology, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, the NetherlandsGIS Technology, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, the Netherlands; GeoTECH Group, CINTECX, Universidade de Vigo, 36310 Vigo, SpainCycloMedia Technology B.V., Waardenburg, the NetherlandsCycloMedia Technology B.V., Waardenburg, the NetherlandsThe Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption.http://www.sciencedirect.com/science/article/pii/S1110016823003770Mobile Laser ScanningLiDAR DataPoint CloudOctomapDynamic Object DetectionDynamic Object Removal |
| spellingShingle | Zhenyu Liu Peter van Oosterom Jesús Balado Arjen Swart Bart Beers Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data Alexandria Engineering Journal Mobile Laser Scanning LiDAR Data Point Cloud Octomap Dynamic Object Detection Dynamic Object Removal |
| title | Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data |
| title_full | Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data |
| title_fullStr | Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data |
| title_full_unstemmed | Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data |
| title_short | Data frame aware optimized Octomap-based dynamic object detection and removal in Mobile Laser Scanning data |
| title_sort | data frame aware optimized octomap based dynamic object detection and removal in mobile laser scanning data |
| topic | Mobile Laser Scanning LiDAR Data Point Cloud Octomap Dynamic Object Detection Dynamic Object Removal |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823003770 |
| work_keys_str_mv | AT zhenyuliu dataframeawareoptimizedoctomapbaseddynamicobjectdetectionandremovalinmobilelaserscanningdata AT petervanoosterom dataframeawareoptimizedoctomapbaseddynamicobjectdetectionandremovalinmobilelaserscanningdata AT jesusbalado dataframeawareoptimizedoctomapbaseddynamicobjectdetectionandremovalinmobilelaserscanningdata AT arjenswart dataframeawareoptimizedoctomapbaseddynamicobjectdetectionandremovalinmobilelaserscanningdata AT bartbeers dataframeawareoptimizedoctomapbaseddynamicobjectdetectionandremovalinmobilelaserscanningdata |