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

@article{Montemerlo2002ConditionalPF,
  title={Conditional particle filters for simultaneous mobile robot localization and people-tracking},
  author={Michael Montemerlo and Sebastian Thrun and William Whittaker},
  journal={Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)},
  year={2002},
  volume={1},
  pages={695-701 vol.1}
}
Presents a probabilistic algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called the conditional particle filter, tracks a large distribution of person locations conditioned upon a smaller distribution of robot poses over time. This method is robust to sensor noise, occlusion, and uncertainty in robot localization. In fact, conditional particle filters can accurately track people in… 

Figures and Tables from this paper

People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters
TLDR
This paper introduces sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects using Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot.
Simultaneous robot Localization and Person Tracking using Rao-Blackwellised Particle Filters with multi-modal sensors
  • K. Qian, Xudong Ma, X. Dai
  • Engineering
    2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2008
TLDR
A probabilistic approach is proposed for Simultaneous robot Localization and Person-Tracking using Rao-Blackwellised particle filters (RBPF), which is capable of tracking human in situations with sensor noise and global uncertainties over the observerpsilas pose, whilst outperforms the conditional particles filters (CPF) in computational efficiency.
Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor
A probabilistic algorithm is proposed for the problem of simultaneous robot localization and people-tracking (SLAP) using single onboard sensor in situations with sensor noise and global
A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment
This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this
Map-Based Multiple Model Tracking of a Moving Object
In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters,
Robust global localization using clustered particle filtering
TLDR
This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot.
Dynamic Maps in Monte Carlo Localization
TLDR
This article presents a method for updating the map dynamically during the process of localization, without requiring a severe increase in running time.
Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters
TLDR
Three efficient implementations of multisensor-human tracking based on different Bayesian estimators, which show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue.
Mobile Robot Pose Estimation Based on Particle Filters for Multi-dimensional State Spaces
TLDR
This paper presents some of the recent innovations on the use of particle filters in robotics.
...
...

References

SHOWING 1-10 OF 18 REFERENCES
Tracking multiple moving targets with a mobile robot using particle filters and statistical data association
TLDR
A sample-based variant of joint probabilistic data association filters is introduced to track features originating from individual objects and to solve the correspondence problem between the detected features and the filters.
Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Monte Carlo Localization with Mixture Proposal Distribution
TLDR
Experimental results with physical robots and an analysis of the formulation of a new proposal distribution for the Monte Carlo sampling step suggest that the new algorithm is significantly more robust and accurate than plain MCL.
Feature based CONDENSATION for mobile robot localization
  • P. Jensfelt, D. Austin, O. Wijk, M. Andersson
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
TLDR
This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner, and some general guidelines are drawn for determining good feature types.
Sensor resetting localization for poorly modelled mobile robots
  • S. Lenser, M. Veloso
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
TLDR
A new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization, is presented, which has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup'99.
CONDENSATION—Conditional Density Propagation for Visual Tracking
TLDR
The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.
Sequential Monte Carlo Methods in Practice
TLDR
This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people
TLDR
W/sup 4/ is a real time visual surveillance system for detecting and tracking people and monitoring their activities in an outdoor environment that employs a combination of shape analysis and tracking to locate people and their parts and to create models of people's appearance so that they can be tracked through interactions such as occlusion.
The Visual Analysis of Human Movement: A Survey
  • D. Gavrila
  • Computer Science
    Comput. Vis. Image Underst.
  • 1999
TLDR
A number of promising applications are identified and an overview of recent developments in this domain is provided, including work on whole-body or hand motion and the various methodologies.
Estimation and Tracking: Principles, Techniques, and Software
Brief review of linear algebra and linear systems brief review of probability theory brief review of statistics some basic concepts in estimation linear estimation in static systems linear dynamic
...
...