Abstract
Aiming at the problem of large positioning accuracy of LiDAR odometry and mapping (LOAM) algorithm, this paper proposes a LOAM algorithm fusion Adaptive Particle Filter (APF) algorithm. Experiments show that the trajectory of the APF algorithm using the LOAM algorithm was half the accuracy of the trajectory using the LOAM algorithm. In order to better verify the accuracy effect, the R-fans 16-line laser radar was used to compare and analyze the test under closed and non-closed routes. The results show that under the closed route, the LOAM algorithm combines the APF algorithm to provide the accurate position trajectory for the car. In the non-closed route, due to the lack of closed-loop constraints, the motion distortion of the car is caused by the accumulation of errors. Through experiments, using the LOAM algorithm to merge the APF algorithm in the non-closed loop could also effectively compensate the motion distortion and filter out the noise, so as to achieve the effect of the trajectory correction, reduce the error with the real trajectory, and improve the positioning accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, X., Pang, J.: Protecting query privacy in location-based services. GeoInformatica 18(1), 95–133 (2014)
Huang, B., Liu, J., Sun, W., et al.: A Robust indoor positioning method based on Bluetooth Low energy with Separate channel information. Sensors 19(16), 3487 (2019)
Xu, H., Ding, Y., Li, P., et al.: An RFID indoor positioning algorithm based on Bayesian probability and K-nearest neighbor. Sensors 17(8), 1806 (2017)
Sharp, I., Yu, K.: Sensor-based dead-reckoning for indoor positioning. Phys. Commun. 13(PA), 4–16 (2014)
Yang, S., Ma, L., Jia, S., et al.: An improved vision-based indoor positioning method. IEEE Access 8, 26941–26949 (2020)
Ma, M., Song, Q., Gu, Y., et al.: An adaptive zero velocity detection algorithm based on multi-sensor fusion for a pedestrian navigation system. Sensors 18(10), 3261 (2018)
Shi, Y., Zhang, W., Yao, Z., et al.: Design of a hybrid indoor location system based on multi-sensor fusion for robot navigation. Sensors 18(10), 3581 (2018)
Gao, Y., Wang, F., Li, J., et al.: Localization of mobile robot based on multi-sensor fusion. In: 2020 Chinese Control And Decision Conference (CCDC), pp. 4367–4372. IEEE (2020)
Li, H.X., Ao, L.H., Guo, H., et al.: Indoor multi-sensor fusion positioning based on federated filtering. Measurement 154, 107506 (2020)
Karam, S., Lehtola, V., Vosselman, G.: Strategies to integrate IMU and LIDAR SLAM for indoor mapping. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 1, 223–230 (2020)
Acknowledgments
The paper was supported by the projects of the National Natural Science Foundation of China (No. 41764002).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, Z., Liao, J., Guo, H., Yu, M. (2021). Lidar/IMU Integrated Navigation and Positioning Method. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-82562-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82561-4
Online ISBN: 978-3-030-82562-1
eBook Packages: Computer ScienceComputer Science (R0)