Active global localization for a mobile robot using multiple hypothesis tracking

By Patric Jensfelt, Steen Kristensen. IEEE Trans. Robotics Autom., 2001

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A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “Other relevant subsequent work can be found in [18], [19], [20], and [21], but also in the related literature of perceptionaware planning [22] and planning under uncertainty [23].”
Robust Monte Carlo localization for mobile robots (2001, methodology): “Nevertheless, multi-hypothesis Kalman filters have applied with great success to various versions of the localization problem, including position tracking and global localization [34,60,61].”
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].”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, result): “This work continues a recent trend found in mobile robot localization, where other researchers successfully applied similar mixture representations (Jensfelt and Kristensen 1999; Reuter 2000; Roumeliotis and Bekey 2000).”
Robotic mapping: a survey (2003, background): “Such representations have been applied with great success in lower dimensional robot localization problems [19, 46, 79, 80], commonly under the name multi hypothesis Kalman filters.”