|
1
|
Ren R, Fu H, Xue H, Li X, Hu X, Wu M. LiDAR‐based robust localization for field autonomous vehicles in off‐road environments. J FIELD ROBOT 2021. [DOI: 10.1002/rob.22031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Ruike Ren
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| | - Hao Fu
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| | - Hanzhang Xue
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| | - Xiaohui Li
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| | - Xiaochang Hu
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| | - Meiping Wu
- College of Intelligence Science and Technology National University of Defense Technology Changsha China
| |
Collapse
|
|
2
|
Abstract
Accurately matching the LIDAR scans is a critical step for an Autonomous Land Vehicle (ALV). Whilst most previous works have focused on the urban environment, this paper focuses on the off-road environment. Due to the lack of a publicly available dataset for algorithm comparison, a dataset containing LIDAR pairs with varying amounts of offsets in off-road environments is firstly constructed. Several popular scan matching approaches are then evaluated using this dataset. Results indicate that global approaches, such as Correlative Scan Matching (CSM), perform best on large offset datasets, whilst local scan matching approaches perform better on small offset datasets. To combine the merits of both approaches, a two-stage fusion algorithm is designed. In the first stage, several transformation candidates are sampled from the score map of the CSM algorithm. Local scan matching approaches then start from these transformation candidates to obtain the final results. Four performance indicators are also designed to select the best transformation. Experiments on a real-world dataset prove the effectiveness of the proposed approach.
Collapse
|