Abstract
This paper proposes a robust algorithm which can realize distributed computing for the problem of multi-agent collaborative localization using relative and absolute observations. Firstly, the relative measurement model of agents is approximated by taking the state of their neighbors as prior knowledge, the approximation error can be modeled as the Gaussian distribution. This is very critical for the algorithm to achieve decentralized computing. Then the iterated kalman filtering algorithm is used to estimate the state for each agent using the information of itself and its neighbors. Finally, the proposed algorithm is compared with other existing approaches. Simulation results show that our algorithm provides better performance in positioning accuracy.
This work is supported by National Natural Science Foundation of China (61803309), Fundamental Research Funds for the Central Universities (3102019ZDHKY02), Shaanxi Provincial Key Research and Development Program (2020ZDLGY06-02), China Postdoctoral Science Foundation (2018M633574), Aviation fund (2019ZA053008), Natural Science Foundation of Shaanxi Province (2019JM-254).
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
Similar content being viewed by others
References
Ching-Chih, T.: A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements. IEEE Trans. Instrum. Meas. 47(5), 1399–1404 (1998)
Kia, S.S., Rounds, S.F., Martinez, S.: A centralized-equivalent decentralized implementation of extended Kalman filters for cooperative localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3761–3766 (2014)
Carrillo-Arce, L.C., et al.: Decentralized multi-robot cooperative localization using covariance intersection. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1412–1417 (2013)
Martinelli, A., Siegwart, R.: Observability analysis for mobile robot localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1471–1476 (2005)
Huang, G.P., et al.: Observability-based consistent EKF estimators for multi-robot cooperative localization. Auton. Robot. 30(1), 99–122 (2011)
Chakraborty, A., et al.: Cooperative localization for fixed wing unmanned aerial vehicles. In: IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 106–117 (2016)
Chakraborty, A., Sharma, R., Brink, K.: Cooperative localization for multi-rotor UAVs. In: AIAA Scitech 2019 Forum, p. 0684 (2019)
Bailey, T., et al.: Decentralised cooperative localisation for heterogeneous teams of mobile robots. In: IEEE International Conference on Robotics and Automation, pp. 2859–2865 (2011)
Boyd, S., et al.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 52(6), 2508–2530 (2006)
Martinelli, A.: Improving the precision on multi robot localization by using a series of filters hierarchically distributed. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1053–1058 (2007)
Wanasinghe, T.R., Mann, G.K., Gosine, R.G.: Distributed leader-assistive localization method for a heterogeneous multirobotic system. IEEE Trans. Autom. Sci. Eng. 12(3), 795–809 (2015)
Luft, L., et al.: Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication. Int. J. Robot. Res. 37(10), 1152–1167 (2018)
Bell, B.M., Cathey, F.W.: The iterated Kalman filter update as a Gauss-Newton method. IEEE Trans. Autom. Control 38(2), 294–297 (1993)
Hu, J., Xie, L., Zhang, C.: Diffusion Kalman filtering based on covariance intersection. IEEE Trans. Signal Process. 60(2), 891–902 (2012)
Tully, S., et al.: Iterated filters for bearing-only SLAM. In: IEEE International Conference on Robotics and Automation 2008, pp. 1442–1448 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tu, K. et al. (2022). Decentralized Collaborative Localization Based on Iterated Kalman Filter Using Relative and Absolute Observations. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_86
Download citation
DOI: https://doi.org/10.1007/978-981-16-9492-9_86
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9491-2
Online ISBN: 978-981-16-9492-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)