Download pdf
Integrating artificial intelligence with SLAM technology for robotic navigation and localization in unknown environments
- 1 Information Science,Trine University,Phoenix, AZ, USA
- 2 Computer Science,Northeastern University,San Jose, CA, USA
- 3 Electrical and Computer Engineering,University of Illinois at Urbana-Champaign,Champaign, Illinois,USA
- 4 Computer Science, Northeastern University, CA, USA
- 5 Business Intelligence,Engineering School of Information and Digital Technologies,Villejuif, France
* Author to whom correspondence should be addressed.
Abstract
In the era of advancing technology, unmanned inspection robots have become indispensable for their efficiency, precision, and safety. Key to their autonomous operation is Simultaneous Localization and Mapping (SLAM) technology, which allows robots to navigate and create maps of unknown environments in real-time. This article explores the integration of SLAM with artificial intelligence, highlighting its role in robotic navigation, localization, and obstacle avoidance. Specifically, we delve into SLAM's principles, its implementation with LiDAR technology, and its application in autonomous robot localization. Furthermore, we introduce a collaborative mapping algorithm based on ORB-SLAM3, enhancing map construction efficiency and real-time performance. Through our exploration, we illustrate the transformative potential of SLAM technology, paving the way for safer and more efficient robotic inspection systems across various industries.
Keywords
SLAM technology, Unmanned inspection robots, Autonomous navigation, LiDAR integration, ORB-SLAM3
[1]. Grisetti, Giorgio, et al. "A tutorial on graph-based SLAM." IEEE Intelligent Transportation Systems Magazine 2.4 (2010): 31-43.
[2]. Chong, T. J., et al. "Sensor technologies and simultaneous localization and mapping (SLAM)." Procedia Computer Science 76 (2015): 174-179.
[3]. Taketomi, Takafumi, Hideaki Uchiyama, and Sei Ikeda. "Visual SLAM algorithms: A survey from 2010 to 2016." IPSJ transactions on computer vision and applications 9 (2017): 1-11.
[4]. Kümmerle, Rainer, et al. "On measuring the accuracy of SLAM algorithms." Autonomous Robots 27 (2009): 387-407.
[5]. Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015). ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE transactions on robotics, 31(5), 1147-1163.
[6]. Wu, Y., Jin, Z., Shi, C., Liang, P., & Zhan, T. (2024). Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis. arXiv preprint arXiv:2403.08217.
[7]. Ball, T., & Rajamani, S. K. (2000). The SLAM toolkit. In Proceedings of CAV’2001 (13th Conference on Computer Aided Verification) (Vol. 2102, pp. 260-264).
[8]. Forster, C., Lynen, S., Kneip, L., & Scaramuzza, D. (2013, November). Collaborative monocular slam with multiple micro aerial vehicles. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3962-3970). IEEE.
[9]. Shi, C., Liang, P., Wu, Y., Zhan, T., & Jin, Z. (2024). Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems. arXiv preprint arXiv:2404.00903.
[10]. Huang, Zengyi, et al. "Application of Machine Learning-Based K-Means Clustering for Financial Fraud Detection." Academic Journal of Science and Technology 10.1 (2024): 33-39.
[11]. Haowei, M. A., et al. "Employing Sisko non-Newtonian model to investigate the thermal behavior of blood flow in a stenosis artery: Effects of heat flux, different severities of stenosis, and different radii of the artery." Alexandria Engineering Journal 68 (2023): 291-300.
[12]. Zou, D., & Tan, P. (2012). Coslam: Collaborative visual slam in dynamic environments. IEEE transactions on pattern analysis and machine intelligence, 35(2), 354-366.
Cite this article
Fan,C.;Li,Z.;Ding,W.;Zhou,H.;Qian,K. (2024). Integrating artificial intelligence with SLAM technology for robotic navigation and localization in unknown environments. Applied and Computational Engineering,77,245-250.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).