3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution
- Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensorPositioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm.…
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| Document Type: | Article (reviewed) |
|---|---|
| Zitierlink: | https://opus.hs-offenburg.de/6162 | Bibliografische Angaben |
| Title (English): | 3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution |
| Author: | Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Markus Obert |
| Year of Publication: | 2022 |
| Date of first Publication: | 2022/09/04 |
| Place of publication: | Basel |
| Publisher: | MDPI |
| First Page: | 1 |
| Last Page: | 19 |
| Article Number: | 154 |
| Parent Title (English): | Computation |
| Editor: | Xiaoqiang Hua |
| Volume: | 10 |
| Issue: | 9 |
| ISSN: | 2079-3197 |
| DOI: | https://doi.org/10.3390/computation10090154 |
| URN: | https://urn:nbn:de:bsz:ofb1-opus4-61629 |
| Language: | English | Inhaltliche Informationen |
| Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
| Institutes: | Bibliografie |
| Tag: | LOAM (LiDAR odometry and mapping); LiDAR; ROS; RTK; SLAM; machine learning; mapping; mobile robotics; navigation; stochastic approach | Formale Angaben |
| Relevance: | Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List |
| Open Access: | Open Access |
| Gold | |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |