Journal of System Simulation
Abstract
Abstract: In the environment with obvious changes in space size, aiming at the drift and other problems of the existing algorithm, Adp-lio-sam mapping method is proposed to adapt to the environment space changes, and improve the generality of lio-sam algorithm. Point cloud dewarping method is improved, and Kalman filter algorithm is used to carry out the motion compensation data by fusing lidar interframe pose interpolation and IMU interpolation. Fuzzy algorithm is used to adapt different points filtering thresholds for different spatial environments and the constraints of loop closure detection are optimized. Experimental results show that, compared with the existing algorithm in the experiments of changing the size of the environment space, the type of features, and the similarity of the structure, the improved algorithm can reduce the loopback error by 47.6% and the z-axis average error by 36.4% with high map quality and good real-time performance.
Recommended Citation
Jiao, Songming; Yao, Xin; Ding, Hui; and Zhong, Yufei
(2023)
"Lidar SLAM Mapping Method Adapted to Environmental Spatial Changes,"
Journal of System Simulation: Vol. 35:
Iss.
8, Article 15.
DOI: 0.16182/j.issn1004731x.joss.22-0489
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss8/15
First Page
1788
Last Page
1798
CLC
TP391.9; TP212
Recommended Citation
Jiao Songming, Yao Xin, Ding Hui, et al. Lidar SLAM Mapping Method Adapted to Environmental Spatial Changes[J]. Journal of System Simulation, 2023, 35(8): 1788-1798.
DOI
0.16182/j.issn1004731x.joss.22-0489
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