Abstract
In order to solve the problems that indoor mobile robots have parking during the traveling process and the Extended Kalman filter (EKF) receives too much influence on parameter selection, this paper proposes an Interacting Multiple Model (IMM)-EKF/Particle Filtering (PF) adaptive algorithm for the tightly inertial navigation system (INS)/Light Detection And Ranging (LiDAR) integrated navigation. The EKF and PF calculate the position of the robot respectively, then the smaller Mahalanobis distance-based filter’s output is selected as the initial value of the next iteration, which improves the accuracy of the positioning for the robot. Based on that, the two motion equations of the static and normal motion models are dsigned at the same time. A Markov chain for converting the two state of the model, and the weighting filtering result of the filtered is used to provide distance estimates. The real experimental results show that the IMM-EKF/PF adaptive algorithm improves the positioning accuracy of mobile robots in the presence of parking.
This work was supported by the Shandong Key Research and Development Program under Grants 2019GGXI04026 and 2019GNC106093.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wu, Z., Wang, W.: INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages. GPS Solut. 23(3), 1–11 (2019). https://doi.org/10.1007/s10291-019-0877-4
Wheeler, D., Koch, D., Jackson, J., Mclain, T., Beard, R.: Relative navigation: a keyframe-based approach for observable GPS-degraded navigation. IEEE Control Syst. 38(4), 30–48 (2018)
Fernndez, A., et al.: ATENEA : Advanced techniques for deeply integrated GNSS/INS/LiDAR navigation. Satellite Navigation Technologies European Workshop on GNSS Signals Signal Processing (2018)
Rafatnia, S., Nourmohammadi, H., Keighobadi, J.: Fuzzy-adaptive constrained data fusion algorithm for indirect centralized integrated SINS/GNSS navigation system. GPS Solut. 23(3), 62 (2019)
Liu, G., Gao, E., Fan., C.: Algorithm of imm combining kalman and particle filter for maneuvering target tracking. Int. J. Inf. Acquisit. 3(04), 321–328 (2006)
Xu, Y., Chen, X.Y., Li, Q.H.: Unbiased tightly-coupled INS/WSN integrated navigation based on extended Kalman filter. J. Chin. Inertial Technol. (2012)
Ming, L., Binzhou, D.: Center, Binzhou College, Inertial/geomagnetic integrated navigation algorithm based IMM-PF
Sovic, V., Athalye, A., Bolic, M., Djuric, P.M.: Particle filtering for indoor RFID tag tracking. Statistical Signal Processing Workshop (2011)
Yan, Y.P., Wong, S.F.: Particle filtering for indoor RFID tag tracking. Cluster Comput. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Feng, N., Zhang, Y., Xu, Y., Bi, S., Liu, T. (2020). LiDAR/DR-Integrated Mobile Robot Localization Employing IMM-EKF/PF Filtering. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-51103-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51102-9
Online ISBN: 978-3-030-51103-6
eBook Packages: Computer ScienceComputer Science (R0)