# Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids

@inproceedings{Burgard1996EstimatingTA, title={Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids}, author={Wolfram Burgard and Dieter Fox and Daniel Hennig and Timo Schmidt}, booktitle={AAAI/IAAI, Vol. 2}, year={1996} }

In order to re-use existing models of the environment mobile robots must be able to estimate their position and orientation in such models. Most of the existing methods for position estimation are based on special purpose sensors or aim at tracking the robot's position relative to the known starting point. This paper describes the position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment. Our method is designed to work…

## 478 Citations

Position tracking with position probability grids

- Computer Science, MathematicsProceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96)
- 1996

An application of position probability grids to the tracking of the position of the robot by matching sensor readings against a metric model of the environment and illustrating the robustness of this method against noisy sensors and errors in the environment.

Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach

- Computer ScienceProceedings. 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.

Markov Localization for Mobile Robots in Dynamic Environments

- Computer ScienceJ. Artif. Intell. Res.
- 1999

A version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments, and includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time.

Approaches to Mobile Robot Localization in Indoor Environments

- Computer Science
- 2001

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.

Position probability grids for mobile robots obtained by convolution

- Computer Science2009 4th International Conference on Autonomous Robots and Agents
- 2000

The paper presents an approach to use relative sensor information for position estimation in an absolute position probability grid using the odometry and nine narrow beam infrared sensors with nonlinear characteristics mounted on a mobile robot.

Template-based state estimation of dynamic objects

- Computer Science1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355)
- 1999

A Bayesian state estimator which computes the maximum likelihood estimate of the state of a dynamic object by matching templates of the object against proximity information obtained by the robot to achieve robust state estimates even while the robot is moving.

Probabilistic state estimation of dynamic objects with a moving mobile robot

- Computer ScienceRobotics Auton. Syst.
- 2001

Markov Localization for Reliable Robot Navigation and People Detection

- Computer ScienceSensor Based Intelligent Robots
- 1998

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.

Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks

- Computer ScienceEngineering Computations
- 2019

A novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.

Monte Carlo localization for mobile robots

- Computer ScienceProceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
- 1999

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.

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