• Corpus ID: 7857083

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… 

Figures from this paper

Position tracking with position probability grids
TLDR
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
  • 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
TLDR
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
TLDR
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
TLDR
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
  • F. Hackbarth
  • Computer Science
    2009 4th International Conference on Autonomous Robots and Agents
  • 2000
TLDR
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
  • D. Schulz
  • Computer Science
    1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355)
  • 1999
TLDR
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
Markov Localization for Reliable Robot Navigation and People Detection
TLDR
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
TLDR
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
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.
...
...

References

SHOWING 1-10 OF 26 REFERENCES
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
TLDR
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.
Probabilistic Robot Navigation in Partially Observable Environments
TLDR
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.
Sensor Fusion in Certainty Grids for Mobile Robots
TLDR
MRL proposes to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves, and can correctly model the fuzziness of each reading and, at the same time, combine multiple measurements to produce sharper map features.
Real-time obstacle avoidance for fast mobile robots in cluttered environments
  • J. Borenstein, Y. Koren
  • Computer Science
    Proceedings., IEEE International Conference on Robotics and Automation
  • 1990
The method described, named the vector field histogram (VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. A
Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing
TLDR
This paper uses the robot's sonar sensors to determine where to capture images and use cues extracted from those images to help perform place recognition, and combines a sonar-based definition of distinctive places with visual information using a simple Bayesian network.
The vector field histogram-fast obstacle avoidance for mobile robots
A real-time obstacle avoidance method for mobile robots which has been developed and implemented is described. This method, named the vector field histogram (VFH), permits the detection of unknown
Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans
TLDR
In this paper an algorithm for tracking position and orientation is presented, it is nearly independent from odometry and its problems with slipping, and derivatives of range-finder scans are calculated which can be used to find position and Orientation by crosscorrelation.
High resolution maps from wide angle sonar
TLDR
The use of multiple wide-angle sonar range measurements to map the surroundings of an autonomous mobile robot deals effectively with clutter, and can be used for motion planning and for extended landmark recognition.
Exploration and model building in mobile robot domains
  • S. Thrun
  • Computer Science
    IEEE International Conference on Neural Networks
  • 1993
TLDR
The first results on COLUMBUS, an autonomous mobile robot, are presented, which aims to explore and model the environment efficiently while avoiding collisions with obstacles using an instance-based learning technique for modeling its environment.
DERVISH - An Office-Navigating Robot
TLDR
A short description of Dervish's hardware and low-level motion modules is presented and this assumptive system, which navigates reliably using retractable assumptions that simplify the planning problem is discussed.
...
...