FastSLAM

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.


References in zbMATH (referenced in 62 articles , 2 standard articles )

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  1. Olsson, Jimmy; Westerborn Alenlöv, Johan: Particle-based online estimation of tangent filters with application to parameter estimation in nonlinear state-space models (2020)
  2. Yu, Fangwen; Shang, Jianga; Hu, Youjian; Milford, Michael: NeuroSLAM: a brain-inspired SLAM system for 3D environments (2019)
  3. Li, Demeng; Zhu, Jihong; Xu, Benlian; Lu, Mingli; Li, Mingyue: An ant-based filtering random-finite-set approach to simultaneous localization and mapping (2018)
  4. Grimmer, Andreas; Clemens, Joachim; Wille, Robert: Formal methods for reasoning and uncertainty reduction in evidential grid maps (2017)
  5. Santhanakrishnan, Manigandan Nagarajan; Rayappan, John Bosco Balaguru; Kannan, Ramkumar: Implementation of extended Kalman filter-based simultaneous localization and mapping: a point feature approach (2017)
  6. Asl, Hamed Jabbari; Yoon, Jungwon: Robust image-based control of the quadrotor unmanned aerial vehicle (2016)
  7. Clemens, Joachim; Reineking, Thomas; Kluth, Tobias: An evidential approach to SLAM, path planning, and active exploration (2016)
  8. Hollósi, Gergely; Lukovszki, Csaba; Moldován, István; Plósz, Sándor; Harasztos, Frigyes: Monocular indoor localization techniques for smartphones (2016)
  9. Speekenbrink, Maarten: A tutorial on particle filters (2016)
  10. Arsenjev, D. G.; Berkovskii, N. A.: Method of adjoint particle filters in nonlinear Bayesian estimation problems with a high prior uncertainty (2015)
  11. Dhiman, Nitin Kumar; Deodhare, Dipti; Khemani, Deepak: \textitWheream I? Creating spatial awareness in unmanned ground robots using SLAM: a survey (2015) ioport
  12. Havangi, R.: Unscented H-infinity filtering based simultaneous localization and mapping with evolutionary resampling (2015)
  13. Othman, Nur Aqilah; Ahmad, Hamzah; Namerikawa, Toru: Sufficient condition for estimation in designing (H_\infty) filter-based SLAM (2015)
  14. Pham, Viet-Cuong; Juang, Jyh-Ching: Robust and efficient SLAM via compressed (H_\infty) filtering (2014)
  15. Roquel, Arnaud; Le Hégarat-Mascle, Sylvie; Bloch, Isabelle; Vincke, Bastien: Decomposition of conflict as a distribution on hypotheses in the framework on belief functions (2014)
  16. Valiente, David; Gil, Arturo; Fernández, Lorenzo; Reinoso, Óscar: Visual SLAM based on single omnidirectional views (2014)
  17. Caro, Luis; Correa, Javier; Espinace, Pablo; Langdon, Daniel; Maturana, Daniel: Indoor mobile robotics at Grima, PUC (2012)
  18. Mastrogiovanni, Fulvio; Sgorbissa, Antonio: How the location of the range sensor affects EKF-based localization (2012)
  19. Munguía, Rodrigo; Grau, Antoni: Monocular SLAM for visual odometry: a full approach to the delayed inverse-depth feature initialization method (2012) ioport
  20. Corke, Peter: Robotics, vision and control. Fundamental algorithms in MATLAB. (2011)

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