The Application of SLAM Based on Extended Kalman Filteration
DOI:
https://doi.org/10.62051/zdk2qj42Keywords:
Extended Kalman Filtering; Simultaneous Localization and Mapping; EKF; SLAM.Abstract
In this paper, there is a brief introduction about Kalman filteration. Then, analyze the working principle of Simultaneous Localization and Mapping, also called Concurrent Localization and Mapping. Give an introduction about different types of SLAM and the sensors required. After then, give some applications to the SLAM. A practical solution of laparoscopic sequence for surgery, which applied some feature that occur in the realistic use and overcome these obstacles. After then introduce a optimizing method for SLAM to be applied to embedded systems, using coprocessor to solve the calculation problem. Then is another algorithm that can be applied with SLAM to adapt different environments in space, which can automatically adjust the output voltage of different component to meet the scarcity of resources in space. Also introduce an application of algorithm to split the mapping of large scale to small scale map, which can increase the smoothness for closing loop route. Then suit the need for realistic world, give conclusion about different way of implementing multiple robots SLAM problem.
Downloads
References
Grasa O G, Civera J, Montiel J M M, et al. EKF monocular SLAM with relocalization for laparoscopic sequences[C], in IEEE International Conference on Robotics and Automation, Shanghai, China, 2011, pp. 4816-4821. DOI: https://doi.org/10.1109/ICRA.2011.5980059
Grasa O, Civera J, Güemes A, et al. EKF monocular slam 3d modeling, measuring and augmented reality from endoscope image sequences[C], in Medical Image Computing and Computer-Assisted Intervention, London, UK, 2009, pp. 425-432.
Civera J, Grasa O G, Davison A J, et al. 1‐Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry. Journal of Field Robotics, 2010, 27(5): 609-631. DOI: https://doi.org/10.1002/rob.20345
Williams B, Klein G, Reid I. Real-time SLAM relocalisation[C], in IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007, pp. 1-8. DOI: https://doi.org/10.1109/ICCV.2007.4409115
Vincke B, Elouardi A, Lambert A. Efficient implementation of EKF-SLAM on a multi-core embedded system[C], in IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, Montreal, 2012, pp. 3049-3054. DOI: https://doi.org/10.1109/IECON.2012.6389411
Saeedi S, Nardi L, Johns E, et al. Application-oriented design space exploration for SLAM algorithms[C], in 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 5716-5723. DOI: https://doi.org/10.1109/ICRA.2017.7989673
Piniés P, Tardós J D. Large-scale slam building conditionally independent local maps: Application to monocular vision. IEEE Transactions on Robotics, 2008, 24(5): 1094-1106. DOI: https://doi.org/10.1109/TRO.2008.2004636
Kudriashov A, Bulatov K, Filatov D, et al. SLAM Techniques Application for Mobile Robot in Rough Terrain[M]. Cham: Springer International Publishing, 2020. DOI: https://doi.org/10.1007/978-3-030-48981-6
Suresh S, Westman E, Kaess M. Through-water stereo SLAM with refraction correction for AUV localization. IEEE Robotics and Automation Letters, 2019, 4(2): 692-699. DOI: https://doi.org/10.1109/LRA.2019.2891486
Downloads
Published
Conference Proceedings Volume
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
