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 54 articles , 2 standard articles )

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  1. Grimmer, Andreas; Clemens, Joachim; Wille, Robert: Formal methods for reasoning and uncertainty reduction in evidential grid maps (2017)
  2. Ingrand, Félix; Ghallab, Malik: Deliberation for autonomous robots: a survey (2017)
  3. Asl, Hamed Jabbari; Yoon, Jungwon: Robust image-based control of the quadrotor unmanned aerial vehicle (2016)
  4. Clemens, Joachim; Reineking, Thomas; Kluth, Tobias: An evidential approach to SLAM, path planning, and active exploration (2016)
  5. Hollósi, Gergely; Lukovszki, Csaba; Moldován, István; Plósz, Sándor; Harasztos, Frigyes: Monocular indoor localization techniques for smartphones (2016)
  6. Speekenbrink, Maarten: A tutorial on particle filters (2016)
  7. Arsenjev, D.G.; Berkovskii, N.A.: Method of adjoint particle filters in nonlinear Bayesian estimation problems with a high prior uncertainty (2015)
  8. Dhiman, Nitin Kumar; Deodhare, Dipti; Khemani, Deepak: \itWhere am I? Creating spatial awareness in unmanned ground robots using SLAM: a survey (2015) ioport
  9. Pham, Viet-Cuong; Juang, Jyh-Ching: Robust and efficient SLAM via compressed $H_\infty$ filtering (2014)
  10. 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)
  11. Caro, Luis; Correa, Javier; Espinace, Pablo; Langdon, Daniel; Maturana, Daniel: Indoor mobile robotics at Grima, PUC (2012)
  12. Mastrogiovanni, Fulvio; Sgorbissa, Antonio: How the location of the range sensor affects EKF-based localization (2012)
  13. Munguía, Rodrigo; Grau, Antoni: Monocular SLAM for visual odometry: a full approach to the delayed inverse-depth feature initialization method (2012) ioport
  14. Corke, Peter: Robotics, vision and control. Fundamental algorithms in MATLAB. (2011)
  15. Gauglitz, Steffen; Höllerer, Tobias; Turk, Matthew: Evaluation of interest point detectors and feature descriptors for visual tracking (2011)
  16. Kleiner, Alexander; Dornhege, Christian: Mapping for the support of first responders in critical domains (2011) ioport
  17. Kuo, Bor-Woei; Chang, Hsun-Hao; Chen, Yung-Chang; Huang, Shi-Yu: A light-and-fast SLAM algorithm for robots in indoor environments using line segment map (2011) ioport
  18. Wu, Hua; Qin, Shi-Yin: An approach to robot SLAM based on incremental appearance learning with omnidirectional vision (2011)
  19. Yang, Shao-Wen; Wang, Chieh-Chih: Simultaneous egomotion estimation, segmentation, and moving object detection (2011)
  20. Gil, Arturo; Mozos, Oscar Martinez; Ballesta, Monica; Reinoso, Oscar: A comparative evaluation of interest point detectors and local descriptors for visual SLAM (2010) ioport

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