Monte Carlo localization for mobile robots

  title={Monte Carlo localization for mobile robots},
  author={Frank Dellaert and Dieter Fox and Wolfram Burgard and Sebastian Thrun},
  journal={Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)},
  pages={1322-1328 vol.2}
  • F. Dellaert, D. Fox, S. Thrun
  • Published 10 May 1999
  • Computer Science
  • Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular the problems encountered are closely related to the type of representation used to represent probability… 

Figures from this paper

Markov Localization for Reliable Robot Navigation and People Detection
This paper presents Markov localization as a technique for estimating the position of a mobile robot based on a fine-grained, metric discretization of the state space, which is able to incorporate raw sensor readings and does not require predefined landmarks.
Probabilistic self-localization for mobile robots
  • C. Olson
  • Computer Science
    IEEE Trans. Robotics Autom.
  • 2000
Probabilistic self-localization techniques for mobile robots that are based on the principle of maximum-likelihood estimation are described, which performs an efficient global search of the pose space that guarantees that the best position is found according to the probabilistic map agreement measure in a discretized pose space.
Approaches to Mobile Robot Localization in Indoor Environments
An extensively tested low-complexity, robust and accurate pose tracking method is presented which utilizes the minimalistic model in combination with a laser sensor, based on the ideas of Multiple Hypothesis Tracking.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches.
Robust Monte Carlo localization for mobile robots
Fast Monte Carlo Localization using spatial density information
This paper proposes an observation model for localization that associates a kernel density estimate (KDE) to each point in the space, independent of orientation, what allows an efficient pre-caching step, and shows that the method is efficient, even working with large sets of particles, and effective.
Improved particle filter based localization and mapping techniques
One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute
Robot localization in symmetric environment
This work presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot.
Localization with Improved Proposals
This paper presents a MCL–based localization system that employs informed proposal distributions to sample particles during the motion step of the filter that is able to estimate the robot’s pose with less uncertainty than the standard MCL implementation.
Effective application of Monte Carlo localization for service robot
This paper introduces the multi-sensor based Monte Carlo localization (MCL) method which represents a robot's belief by a set of weighted samples and use the laser range finder (LRF) sensor to measurement update.


Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach
  • W. Burgard, Andrcas Derr, D. Fox, A. Cremers
  • Computer Science
    Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)
  • 1998
The dynamic Markov localization technique is presented as a uniform approach to position estimation, able to globally estimate the position of the robot, to efficiently track its position whenever the robot's certainty is high, and to detect and recover from localization failures.
Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids
The position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment is described, designed to work with standard sensors and is independent of any knowledge about the starting point.
An experimental comparison of localization methods
This experimental study compares two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which using Kalman filtering techniques based on matching sensor scans.
Probabilistic Robot Navigation in Partially Observable Environments
First results are reported on first results of a research program that uses par tially observable Markov models to robustly track a robots location in office environments and to direct its goal-oriented actions.
A comparison of position estimation techniques using occupancy grids
  • B. Schiele, J. Crowley
  • Engineering
    Proceedings of the 1994 IEEE International Conference on Robotics and Automation
  • 1994
Experimental results show that matching of segments extracted from the both the local and global occupancy grids gives results which are superior to a direct matching of grids, or to a mixed match of segments to grids.
AMOS: comparison of scan matching approaches for self-localization in indoor environments
  • J.-S. Gutmann, C. Schlegel
  • Computer Science
    Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96)
  • 1996
This paper describes results from evaluating different self-localization approaches in indoor environments for mobile robots based on 2D laser scans and an odometry position estimate and shows that the position error can be kept small enough to perform navigation tasks.
Acting under uncertainty: discrete Bayesian models for mobile-robot navigation
The optimal solution to the problem of how actions should be chosen is presented, formulated as a partially observable Markov decision process, which goes on to explore a variety of heuristic control strategies.
Contour Tracking by Stochastic Propagation of Conditional Density
The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object position and shape, over time, and is markedly superior to what has previously been attainable from Kalman filtering.
An improved particle filter for non-linear problems
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other
Directed Sonar Sensing for Mobile Robot Navigation
This paper presents a Sonar Sensor Model for Directed Sensing Strategies, which combines model-Based Localization, Simultaneous Map Building, and Simultaneously Map Building and Localization.