Abstract
In view of the technical difficulties of autonomous navigation in local areas, this paper proposes a high-precision autonomous navigation shared balancing bike system based on EKF-SLAM. This system uses the EKF-SLAM algorithm in robot localization to achieve simultaneous localization and map construction using the extended Kalman filter. At the same time, GPS and IMU are also employed for absolute positioning, and point cloud matching is used for relative positioning to achieve multisensor fusion positioning. For the convenience of users, this system uses the RNN-T model for speech recognition destinations. Through experimental verification, the EKF-SLAM-based autonomous navigation technology proposed in this paper can meet the accurate localization service and can realize the function of high precision autonomous navigation and voice recognition of destinations for shared balancing vehicles in a local area.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhifu, M., D’Maris, C.: The sharing economy promotes sustainable societies. Nat. Commun. 10(1), 1214–1217 (2019)
Pascacio, P., et al.: Collaborative indoor positioning systems: a systematic review. Sensors 21(3), 1002 (2021)
Kiss-Illés, D., Barrado, C., Salamí, E.: GPS-SLAM: an augmentation of the ORB-SLAM algorithm. Sensors 19(22), 4973 (2019)
Feng, D., et al.: Kalman filter based integration of IMU and UWB for high-accuracy indoor positioning and navigation. IEEE Internet Things J. 7(4), 3133–3146 (2020)
Kokovkina, V.A., et al.: The algorithm of EKF-SLAM using laser scanning system and fisheye camera. In: 2019 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), pp. 1–6 (2019)
Shen, L., Stopher, P.R.: Review of GPS travel survey and GPS data-processing methods. Transp. Rev. 34(3), 316–334 (2014)
Girbes-Juan, V., et al.: Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles. IEEE Trans. Veh. Technol. 70(9), 8617–8626 (2021)
Gojcic, Z., et al.: The perfect match: 3D point cloud matching with smoothed densities. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5540–5549 (2019)
Park, D.S., et al.: SpecAugment: a simple data augmentation method for automatic speech recognition, pp. 98–105 (2019). arXiv preprint arXiv:1904.08779
Saon, G., et al.: Advancing RNN transducer technology for speech recognition. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5654–5658 (2021)
Chen, G.: Exploration and research on computer aided translation technology and English translation practice. J. Phys: Conf. Ser. 1213(4), 042011 (2019)
Abdulredah, S.H., Kadhim, D.J.: Developing a real time navigation for the mobile robots at unknown environments. Indon. J. Electr. Eng. Comput. Sci. 20(1), 500–509 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cheng, X., Wang, Y., Guo, D., Li, X., Gao, Y. (2023). Research on High Precision Autonomous Navigation of Shared Balancing Vehicles Based on EKF-SLAM. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1880. Springer, Singapore. https://doi.org/10.1007/978-981-99-5971-6_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-5971-6_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5970-9
Online ISBN: 978-981-99-5971-6
eBook Packages: Computer ScienceComputer Science (R0)