NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering
| dc.contributor.author | Courtois, Hugo | |
| dc.contributor.author | Aouf, Nabil | |
| dc.contributor.author | Ahiska, Kenan | |
| dc.contributor.author | Cecotti, Marco | |
| dc.date.accessioned | 2023-05-02T13:57:41Z | |
| dc.date.available | 2023-05-02T13:57:41Z | |
| dc.date.issued | 2023-02-28 | |
| dc.description.abstract | The Normal Distribution Transform Occupancy Map (NDT OM) is a mapping algorithm able to represent a dynamic 3D environment. The resulting map has fixed boundaries, thus a robot with unbounded displacement might fall outside of the map due to memory limitation. In this paper, a recentering algorithm called NDT RC is proposed to avoid this issue. NDT RC extends the use of NDT OM for vehicles with unbounded displacements. NDT RC provides a seamless translation of the map as the robot gets far from the center of the previous map. The influence of NDT RC on the precision of the estimated trajectory of the robot, or odometry, is examined on two publicly available datasets, the KITTI and Ford datasets. An analysis of the sensitivity of the NDT RC to its tuning parameters is carried out using the Ford dataset, while the KITTI dataset is used to measure the influence of the density of the input point cloud. The results show that the proposed recentering strategy improves the accuracy of the odometry calculated by registering the latest lidar scan on the generated map compared to other NDT based approaches (NDT OM, NDT OM Fusion, SE-NDT). In particular, the proposed method, which does not perform loop closure, reduces the mean absolute translation error by 16% and the runtime by 88% compared to the NDT OM Fusion on the Ford dataset. | en_UK |
| dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC): EP/K504324/1 | en_UK |
| dc.identifier.citation | Courtois H, Aouf N, Ahiska K, Cecotti M. (2024) NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering. IEEE Transactions on Intelligent Vehicles, Volume 9, Issue 1, January 2024, pp. 2999-3009 | en_UK |
| dc.identifier.eissn | 2379-8904 | |
| dc.identifier.issn | 2379-8858 | |
| dc.identifier.uri | https://doi.org/10.1109/TIV.2023.3250326 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19565 | |
| dc.language.iso | en | en_UK |
| dc.publisher | IEEE | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | NDT | en_UK |
| dc.subject | occupancy mapping | en_UK |
| dc.subject | KITTI dataset | en_UK |
| dc.subject | Ford dataset | en_UK |
| dc.subject | recentering | en_UK |
| dc.title | NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering | en_UK |
| dc.type | Article | en_UK |