Multi-Constellation-Inspired Single-Shot Global LiDAR Localization
Authors: Tongzhou Zhang, Gang Wang, Yu Chen, Hai Zhang, Jue Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on the KITTI dataset and the self-collected dataset demonstrate that our method achieves an average localization error (including errors in the z-axis) of 0.89 meters. In addition, it achieves retrieval efficiency of 0.357 s per frame on the former dataset and 0.214 s per frame on the latter one. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University 2College of Software, Jilin University 3State Key Laboratory of Automotive Simulation and Control, Jilin University 4Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University 5National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology |
| Pseudocode | Yes | Algorithm 1: planar point downsampling Input: sorted planar point set Q, first point q0 in set Q Output: the downsampled set Q |
| Open Source Code | Yes | Code and data are available at https://github.com/jlurobot/multiconstellation-localization. |
| Open Datasets | Yes | KITTI Dataset. KITTI dataset is released in (Geiger, Lenz, and Urtasun 2012), which provides 3D point clouds generated by Velodyne-HDL64e Li DAR and ground truth provided by Ox TS-RT3000. |
| Dataset Splits | No | The paper mentions splitting sequences for mapping and localization tasks and selecting observation points, but it does not specify explicit training/validation/test splits with percentages or counts for reproducibility beyond the general use of the KITTI and self-collected datasets. |
| Hardware Specification | Yes | All experiments are conducted on a computer equipped with an Intel Core i7-1165G7 processor and 32GB of RAM. |
| Software Dependencies | No | The paper states: 'All methods are implemented in C++ and executed on Ubuntu Linux.' This does not provide specific version numbers for C++ compiler, Ubuntu version, or any libraries used, which are necessary for full reproducibility. |
| Experiment Setup | Yes | We adopt the strategy proposed by Shi et al. (Shi et al. 2021), employing the scan context for coarse localization in the first stage. Subsequently, comparative experiments are conducted using the methods introduced in this paper, alongside ICP, NDT, GICP, and KISS-ICP, in the second stage. For simplicity, these comparative methods are refered as Sc-Icp, Sc-Ndt, Sc-Gicp, and Sc-Kicp, respectively. Because of the need for a pre-built map in these comparative methods, LIO-SAM (Shan et al. 2020) is employed in this paper, and global navigation satellite system (GNSS) factors are incorporated for mapping. Sequences 02 , 05 , and 07 are individually divided into two parts. Starting from the initial frame of each sequence, the point cloud is employed for mapping every 0.2 second, while the remaining data is dedicated to localization tasks. Moreover, The number of observed points in the proposed method is set to 5 (the preceding two and the subsequent two). |