Sensor resetting localization for poorly modelled mobile robots

By S. Lenser, Manuela M. Veloso. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000

352 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8682f7e182b83ce0c317412a5a37fd05e4fc7a4a

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Citations in the corpus

Planning algorithms (2006, background): “For further reading on probabilistic localization, see [43, 260, 424, 450, 488, 496, 552, 624, 625, 757, 828, 888, 889, 962].”
People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters (2003, background): “…and Jepson 1998; Isard and Blake 1996), mobile robot localization (Dellaert et al. 1999; Fox et al. 1999, 2001; Gross, Boehme, and König 2002; Lenser and Veloso 2000; Montemerlo et al. 2002; Montemerlo, Thun, and Whittaker 2002; Murphy and Russell 2001; Vlassis, Terwijn, and Kröse 2002;…”
WiFi localization and navigation for autonomous indoor mobile robots (2010, methodology): “The number of particles to be resampled N is calculated based on Sensor Resetting Localization [12], subject to a maximum Nmax and minimum Nmin.”
Distributed Multirobot Exploration and Mapping (2005, background): “Particle filters have been applied with great success to this problem [7], [8], [15], [21].”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, methodology): “However, MCL can be applied if it is known that one robot starts in the map built by another.”
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization (2005, methodology): “They range from the insertion of random samples [25] to techniques that use the most recent observations to more intelligently insert samples at potential positions of the robot [ 15 ], [34].”
Sub-meter indoor localization in unmodified environments with inexpensive sensors (2010, methodology): “Several papers have utilized MCL with cameras including [10], [11], [12], [13], [14].”
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (2000, methodology): “The Monte Carlo localization algorithm (MCL) is a version of Markov localization that uses samples to approximate the belief b (Dellaert, Burgard, et al. 1999; Dellaert, Fox, et al. 1999; Denzler, Heigl, and Niemann 1999; Fox et al. 1999; Lenser and Veloso 2000).”
Robust Monte Carlo localization for mobile robots (2001, methodology): “Other fi xes include the addition of randomsamples into the posterior [18], and the generation of samples at locations that are consistent with the sensor readings [37]—a strat egy that is similar to Mixture-MCL below but, without proper sample weighting, is mathematically unmotivated.”
Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information (2017, background): “To solve this issue, authors in [22] proposed sensor resetting, which adds new samples according to the current measurement likelihood.”
Probabilistic Algorithms in Robotics (2000, background): “The MCL algorithm is in fact quite efﬁcient; slight modiﬁcations of the basic algorithms [56, 92] require as few as 100 samples for reliable localization, consuming only a small fraction of time available on low-end PC.”
Map-Based Precision Vehicle Localization in Urban Environments (2007, methodology): “GPS, when available, is also used in the calculation of the measurement likelihood to reduce the danger of particles moving too far away from the GPS location.”