Conditional particle filters for simultaneous mobile robot localization and people-tracking

By Michael Montemerlo, Sebastian Thrun, William L. Whittaker. Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002

372 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/61b4748c51aae49050755120eedaaf979613747f

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Citations in the corpus

Learning Motion Patterns of People for Compliant Robot Motion (2005, methodology): “The techniques developed by Schulz et al. (2003) and Montemerlo, Thrun, and Whittaker (2002) are able to robustly track multiple persons in the sensor range of a mobile robot equipped with laserrange finders using particle filters.”
FastSLAM: a factored solution to the simultaneous localization and mapping problem (2002, background): “Localization and people tracking form a chicken-or-egg problem very similar to the SLAM problem [44].”
Robotic mapping: a survey (2003, background): “Such algorithms are popular in the target tracking literature [4, 68, 85].”
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas (2003, neutral): “In indoor environments, the most important targets of the DATMO problem are people [6] [8] [9] [11] [16].”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, methodology): “Motivated by particle filtering algorithm [25], we represent this distribution as a set of weighted particles, where each particle is a sampled ATCRF.”
Multisensor-Based Human Detection and Tracking for Mobile Service Robots (2009, background): “between performance and computational cost, giving good results in most of the cases where the set of entities to track is not too dense [18], [19].”
HERB: a home exploring robotic butler (2010, background): “There has been significant work done with people tracking ( Montemerlo and Thrun 2002 ), and Detecting and Tracking Moving Objects (DATMO) (Mendes et al. 2004; Wang and Thorpe 2002).”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, background): “…speech recognition (Vermaak et al. 2002), mobile robot localization (Fox et al. 1999a; Lenser and Veloso 2000; Jensfelt et al. 2000), map building (Montemerlo et al. 2002a), people tracking (Schulz et al. 2001; Montemerlo, Thrun, and Whittaker 2002b), and fault detection (Verma, Langford, and…”
Map building with mobile robots in dynamic environments (2003, background): “[15] present an approach to simultaneous localization and people tracking.”
Natural person-following behavior for social robots (2007, background): “Montemerlo and colleagues developed a laser-based persontracker for mobile robots [12]; however, their tracker requires the robot to have an a priori map of the environment, and will thus not work in a new or highly dynamic environment.”