Probabilistic Algorithms in Robotics

By Sebastian Thrun. The AI Magazine, 2000

518 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c0bd664584b68dea3bf21f80d451b4892c16c515

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Citations in the corpus

A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks (2004, methodology): “The collected information is the model where each point is mapped to either a signal strength vector [4, 14, 15, 19] or a signal strength probability distribution [6, 18, 22, 25].”
Visual Navigation for Mobile Robots: A Survey (2008, methodology): “[34, 129, 130] Indoor Map based Absolute localization”
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (2000, background): “Our central conjecture is that the probabilistic approach is a viable solution to a large range of robot problems involving sensing in the physical world (Thrun 2000).”
Bayesian indoor positioning systems (2005, background): “Work on so-called “particle filters is relevant - see, for example, [26], [6], [19], [4].”
Hierarchical Dirichlet Processes (2006, background): “Clustering problems can be approached within a probabilistic framework via finite mixture models (and their dynamical cousins the HMM), and recent years have seen numerous examples of applications of finite mixtures and HMMs in areas such as bioinformatics (Durbin et al. 1998), speech recognition (H”
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System (2008, methodology): “In other work, we have stored behavioral and temporal data in the transition tuple to aid path planning [6], [ 38 ], and frequency of use data in boththeexperienceandtransitiontuplestoaidmapmaintenance [38].”
The vSLAM Algorithm for Robust Localization and Mapping (2005, background): “In particular, the SICK TM Laser Range Finder (LRF) was a milestone for autonomous robotics and navigation, and the discovery of probabilistic robotics [9] was a breakthrough.”
Survey of Wireless Indoor Positioning Techniques and Systems (2007, background): “assisting wireless localization has been studied by many researchers [43].”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, background): “Virtually all such algorithms can be directly derived from this equation (see also Thrun 2000).”
Planning algorithms (2006, background): “In practice, it appears to work well even under cases that are not theoretically guaranteed to converge [940].”
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “Robot localization [111] has leveraged the motion information to improve wireless localization.”
Vision for Mobile Robot Navigation: A Survey (2002, methodology): “The resulting ambiguities in localization may be resolved by methods such as: Markov localization [145], partially observable Markov processes [137], Monte Carlo localization [60], [32]), multiplehypothesis Kalman filtering based on a mixture of Gaussians [27], using intervals for representing…”
Robotics-Based Location Sensing Using Wireless Ethernet (2002, methodology): “Many excellent examples of this method exist in the literature [35].”
Localization of wireless sensor networks with a mobile beacon (2004, background): “Many of the outdoor localization systems rely heavily on infrastructure.”