EVALUATION OF LIDAR ODOMETRY AND MAPPING BASED ON REFERENCE LASER SCANNING

被引：3
作者：
Koszyk, J. [1 ]
Labedz, P. [2 ]
Grzelka, K. [3 ]
Jasinska, A. [3 ]
Pargiela, K. [3 ]
Malczewska, A. [3 ]
Strzabala, K. [3 ]
Michalczak, M. [3 ]
Ambrozinski, L. [1 ]
机构：
[1] AGH Univ Krakow, Dept Robot & Mechatron, Krakow, Poland
[2] Cracow Univ Technol, Fac Comp Sci & Telecommun, Krakow, Poland
[3] AGH Univ Krakow, Fac Geodata Sci Geodesy & Environm Engn, Krakow, Poland
来源：
2ND GEOBENCH WORKSHOP ON EVALUATION AND BENCHMARKING OF SENSORS, SYSTEMS AND GEOSPATIAL DATA IN PHOTOGRAMMETRY AND REMOTE SENSING, VOL. 48-1 | 2023年
关键词：
SLAM; LiDAR-SLAM; mobile robot; LeGO-LOAM; ICP;
D O I：
10.5194/isprs-archives-XLVIII-1-W3-2023-79-2023
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Simultaneous localization and mapping (SLAM) is essential for the robot to operate in an unknown, vast environment. LiDAR-based SLAM can be utilizable in environments where other sensors cannot deliver reliable measurements. Providing accurate map results requires particular attention due to deviations originating from the device. This study is aimed to assess LiDAR-based mapping quality in a vast environment. The measurements are conducted on a mobile platform. Accuracy of the map collected with the LeGO-LOAM method was performed by making a comparison to a map gathered with geodetic scanning using ICP. The results provided 60% of fitted points in a distance lower than 5 cm and 80% in a distance lower than 10 cm. The findings prove the mileage of the map created with this method for other tasks, including autonomous driving and semantic segmentation.
引用
收藏
页码：79 / 84
页数：6
相关论文
共 50 条
[21]   Mergeable Probabilistic Voxel Mapping for LiDAR-Inertial-Visual Odometry [J].
Wang, Balong ;
Bessaad, Nassim ;
Xu, Huiying ;
Zhu, Xinzhong ;
Li, Hongbo .
ELECTRONICS, 2025, 14 (11)
[22]   OW-LOAM: Observation-Weighted LiDAR Odometry and Mapping [J].
Zhang, Zhuo ;
Yao, Zheng ;
Lu, Mingquan .
2022 IEEE 12TH INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN 2022), 2022,
[23]   WiCRF: Weighted Bimodal Constrained LiDAR Odometry and Mapping With Robust Features [J].
Chang, Dengxiang ;
Zhang, Runbang ;
Huang, Shengjie ;
Hu, Manjiang ;
Ding, Rongjun ;
Qin, Xiaohui .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (03) :1423-1430
[24]   Semantic Lidar Odometry and Mapping for Mobile Robots Using RangeNet plus [J].
Dong, Xiangyu ;
He, Guo ;
Fan, Peipei ;
Zhang, Fei ;
Li, Teng ;
Zhou, Junyi ;
Xie, Jia ;
Zhang, Junjie ;
Huang, Jie ;
Shang, Weiwei .
PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2022), 2022, :721-726
[25]   Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry [J].
Yuan, Chongjian ;
Xu, Wei ;
Liu, Xiyuan ;
Hong, Xiaoping ;
Zhang, Fu .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (03) :8518-8525
[26]   Spatial LiDAR odometry and mapping for complex agricultural environments - Spatial FieldLOAM [J].
Rakun, Jurij ;
Duchon, Frantisek ;
Lepej, Peter .
BIOSYSTEMS ENGINEERING, 2024, 248 :58-72
[27]   AGCV-LOAM: Air-Ground Cross-View based LiDAR Odometry and Mapping [J].
Zhu, Minzhao ;
Yang, Yi ;
Song, Wenjie ;
Wang, Meiling ;
Fu, Mengyin .
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, :5261-5266
[28]   A 3D Lidar Odometry and Mapping Method Based on Clustering-Directed Feature Point [J].
Su, Yukang ;
Zhang, Shuo ;
Zhang, Chengning ;
Wu, Yuyang .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (12) :18391-18401
[29]   FEVO-LOAM: Feature Extraction and Vertical Optimized Lidar Odometry and Mapping [J].
Wang, Zelin ;
Yang, Limin ;
Gao, Feng ;
Wang, Liangyu .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (04) :12086-12093
[30]   RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs [J].
Yang, Jung-Cheng ;
Lin, Chun-Jung ;
You, Bing-Yuan ;
Yan, Yin-Long ;
Cheng, Teng-Hu .
SENSORS, 2021, 21 (12)
12345