Abstract
This chapter presents a new distributed cooperative localization technique based on a second-order sensor fusion method developed for the special Euclidean group. Uncertainties in the robot pose, sensor measurements, and landmark positions (neighboring robots in this case) are modeled as Gaussian distributions in exponential coordinates. This proves to be a better fit for both the prior and posterior distributions resulting from the motion of nonholonomic kinematic systems with stochastic noise (as compared to standard Gaussians in Cartesian coordinates). We provide a recursive closed-form solution to the multi-sensor fusion problem that can be used to incorporate a large number of sensor measurements into the localization routine and can be implemented in real time. The technique can be used for nonlinear sensor models without the need for further simplifications given that the required relative pose and orientation information can be provided, and it is scalable in that the computational complexity does not increase with the size of the robot team and increases linearly with the number of measurements taken from nearby robots. The proposed approach is validated with simulation in Matlab.
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References
Barooah P, Russell J, Hespanha J (2010) Approximate distributed Kalman filtering for cooperative multi-agent localization. In: Distributed computing in sensor systems, vol 6131. Springer, Heidelberg, pp 102–115
Caglioti V, Citterio A, Fossati A (2006) Cooperative, distributed localization in multi-robot systems: a minimum-entropy approach. In: Distributed intelligent systems: collective intelligence and its applications, pp 20–30
Carlone L, Kaouk M, Du J, Bona B, Indri M (2011) Simultaneous localization and mapping using Rao-Blackwellized particle filters in multi robot systems. J Intell Robot Syst 63:283–307
Chirikjian G (2012) Stochastic models, information theory, and lie groups, vol 2. Birkhäuser, Boston
Chirikjian G, Kobilarov M (2014) Gaussian approximation of non-linear measurement models on Lie groups. IEEE conference on decision and control, pp 6401–6406
Fox D, Burgard W, Kruppa H, Thrun S (2000) A probabilistic approach to collaborative multi-robot localization. Auton Robot 8(3):325–344
Higham H (2001) An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Rev 43:525–546
Howard A (2006) Multi-robot simultaneous localization and mapping using particle filters. Int J Robot Res 25(12):1243–1256
Liu J, Yuan K, Zou W, Yang Q (2005) Monte Carlo multi-robot localization based on grid cells and characteristic particles. In: International conference on advanced intelligent mechatronics, pp 510–515
Long A, Wolfe K, Mashner M, Chirikjian G (2012) The banana distribution is Gaussian: a localization study with exponential coordinates. In: Robotics: science and systems
Marjovi A, Marques L, Penders J (2009) Guardians robot swarm exploration and firefighter assistance. In: IEEE/RSJ international conference on intelligent robots and systems: workshop on network robot systems
Mourikis AI, Roumeliotis SI (2006) Performance analysis of multirobot cooperative localization. IEEE Trans Robot 22(4):666–681
Park W, LIu Y, Zhou Y, Moses M, Chirikjian G (2008) Kinematic state estimation and motion planning for stochastic nonholonomic systems using the exponential map. Robotica 26:419–434
Roumeliotis S, Bekey G (2002) Distributed multirobot localization. IEEE Trans Robot Autom 18(5):781–795
Thrun S, Burgard W, Fox D (2006) Probabilistic robotics. MIT Press, Cambridge
Wang Y, Chirikjian G (2008) Nonparametric second-order theory of error propagation on motion groups. Int J Robot Res 27(11–12):1258–1273
Acknowledgements
This work was supported by NSF Grant RI-Medium: IIS-1162095.
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Li, X., Chirikjian, G.S. (2016). Lie-Theoretic Multi-Robot Localization. In: Turaga, P., Srivastava, A. (eds) Riemannian Computing in Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-319-22957-7_8
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DOI: https://doi.org/10.1007/978-3-319-22957-7_8
Publisher Name: Springer, Cham
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