Particle Filters for Mobile Robot Localization

By Dieter Fox, Sebastian Thrun, Wolfram Burgard, Frank Dellaert. Sequential Monte Carlo Methods in Practice, 2001

433 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/34ddfb4e3aac6fc0a729bfb9d9b65b0b0a963f16

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In Aerospace Engineering

In Artificial Intelligence

Citations in the corpus

Planning algorithms (2006, background): “11: Four frames from an animation that performs probabilistic localization of an indoor mobile robot using sonars [352].”
The vSLAM Algorithm for Robust Localization and Mapping (2005, methodology): “By also using a localization scheme with a particle filter [2] and an adaptive mixed proposal distribution [3], [5], vSLAM offers a breakthrough SLAM algorithm that allows navigation with good accuracy in various real-world environments.”
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, background): “For example, Thrun et. al., tracked people with high granularity laser range finders and sensor collision did not occur unless people occluded each other very closely [ 70 ].”
Particle filter theory and practice with positioning applications (2010, background): “the prior and if it is integrable in xk [ 52 ] , then”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “We used a beam-based sensor model to compute the likelihood of the sensor scans [30, 31 ].”
WiFi localization and navigation for autonomous indoor mobile robots (2010, methodology): “The recursive Bayesian update for a location belief B(lt) for location l at time step t using sensor readings St and odometry readings ut−1 [11] is given by:”
The evolution of robotics research (2007, background): “A discussion of their properties can be found in [ 29 ].”
Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates (2016, methodology): “For example, a strategy is to use one proposal using the state equation and the other using the most recent observation (Elinas et al., 2006; Thrun et al., 2001b).”
Pedestrian localisation for indoor environments (2008, background): “Particle filter implementations have been shown to achieve superior accuracy relative to grid-based approaches, whilst requiring an order of magnitude less computation and memory [71].”
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization (2005, background): “In the past a variety of different particle filter algorithms have been developed and many variants have been applied with great success to various application domains [27], [28], [29], [30], [31], [32], [33], [17].”
Convergence of Sequential Monte Carlo Methods (2007, neutral): “; see ( Fox, Thrun, Burgard & Dellaert, 2001 ) for an alternative”
An Introduction to MCMC for Machine Learning (2004, background): “In doing so, the importance weights become equal to the transition prior (Fox et al., 2001).”
Particle filters for positioning, navigation, and tracking (2002, background): “Another related application fitting this framework, not explicitly included here, is robot localization; see, for instance, [43] and [44].”