VS-SLAM: Robust SLAM Based on LiDAR Loop Closure Detection with Virtual Descriptors and Selective Memory Storage in Challenging Environments
Abstract
:1. Introduction
- To mitigate the sensitivity of existing descriptors to translational changes, we propose a novel virtual descriptor technique that enhances translational invariance and improves loop closure detection accuracy.
- To further improve the accuracy of loop closure detection in structurally similar environments, we propose an efficient and reliable selective memory storage technique based on scene recognition and key descriptor evaluation, which also reduces the memory consumption of the loop closure database.
- Based on the two proposed techniques, we developed a LiDAR SLAM system with loop closure detection capability, which maintains high accuracy and robustness even in challenging environments with structural similarity.
- Experimental results in self-built simulation, real-world environments, and public datasets demonstrate that VS-SLAM outperforms state-of-the-art methods in terms of memory efficiency, accuracy, and robustness.
2. Related Work
2.1. Sensitivity of LiDAR Descriptors to Translation Changes
2.2. Accuracy and Memory Consumption of Loop Closure Detection
3. System Framework
4. Methods
4.1. Virtual Descriptor Technique
4.2. Selective Memory Storage Technology
4.2.1. Scene Recognition
4.2.2. Key Descriptor Evaluation
4.2.3. Selective Memory Storage
5. Experiments
5.1. Selective Memory Storage Technology Testing
5.2. Comparison of Localization Accuracy and Ablation Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sequence | SC-LVI-SAM [9] | VS-SLAM-w/o-st | VS-SLAM | |||
---|---|---|---|---|---|---|
D.C. | M.S. | D.C. | M.S. | D.C. | M.S. | |
Gazebo-Corridor-01 | 200 | 1048 | 202 | 1048 | 17 | 152 |
Gazebo-Corridor-02 | 212 | 1048 | 215 | 1048 | 17 | 152 |
M2DGR-Gate-01 | 211 | 1048 | 218 | 1048 | 5 | 56 |
M2DGR-Gate-02 | 419 | 2072 | 418 | 2072 | 25 | 152 |
M2DGR-Gate-03 | 357 | 2072 | 360 | 2072 | 17 | 152 |
M2DGR-Street-03 | 591 | 4120 | 590 | 4120 | 11 | 88 |
M2DGR-Street-04 | 1362 | 8216 | 1355 | 8216 | 46 | 280 |
M2DGR-Street-08 | 587 | 4120 | 580 | 4120 | 16 | 88 |
Real-Corridor-01 | 434 | 2072 | 432 | 2072 | 23 | 152 |
Real-Corridor-02 | 423 | 2072 | 431 | 2072 | 21 | 152 |
Sequence | LVI-SAM [28] | LVI-SAM- w/o-loop [28] | SC-LVI-SAM [9] | VS-SLAM- w/o-vd | VS-SLAM |
---|---|---|---|---|---|
Gazebo-Corridor-01 | 5.58 | 0.77 | 6.18 | 0.75 | 0.67 |
Gazebo-Corridor-02 | 12.20 | 1.08 | 7.04 | 1.10 | 1.05 |
M2DGR-Gate-01 | 4.58 | 2.26 | 0.14 | 0.14 | 0.13 |
M2DGR-Gate-02 | 0.30 | 0.30 | 0.31 | 0.31 | 0.30 |
M2DGR-Gate-03 | 0.15 | 0.15 | 0.15 | 0.15 | 0.14 |
M2DGR-Street-03 | 0.15 | 0.15 | 0.15 | 0.15 | 0.13 |
M2DGR-Street-04 | 1.73 | 1.71 | 1.64 | 1.67 | 1.63 |
M2DGR-Street-08 | 10.56 | 2.47 | 0.71 | 0.71 | 0.65 |
Real-Corridor-01 | 0.18 | 3.06 | 49.59 | 0.14 | 0.11 |
Real-Corridor-02 | 0.19 | 2.18 | 56.61 | 0.16 | 0.10 |
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Song, Z.; Zhang, X.; Zhang, S.; Wu, S.; Wang, Y. VS-SLAM: Robust SLAM Based on LiDAR Loop Closure Detection with Virtual Descriptors and Selective Memory Storage in Challenging Environments. Actuators 2025, 14, 132. https://doi.org/10.3390/act14030132
Song Z, Zhang X, Zhang S, Wu S, Wang Y. VS-SLAM: Robust SLAM Based on LiDAR Loop Closure Detection with Virtual Descriptors and Selective Memory Storage in Challenging Environments. Actuators. 2025; 14(3):132. https://doi.org/10.3390/act14030132
Chicago/Turabian StyleSong, Zhixing, Xuebo Zhang, Shiyong Zhang, Songyang Wu, and Youwei Wang. 2025. "VS-SLAM: Robust SLAM Based on LiDAR Loop Closure Detection with Virtual Descriptors and Selective Memory Storage in Challenging Environments" Actuators 14, no. 3: 132. https://doi.org/10.3390/act14030132
APA StyleSong, Z., Zhang, X., Zhang, S., Wu, S., & Wang, Y. (2025). VS-SLAM: Robust SLAM Based on LiDAR Loop Closure Detection with Virtual Descriptors and Selective Memory Storage in Challenging Environments. Actuators, 14(3), 132. https://doi.org/10.3390/act14030132