Metric-based iterative closest point scan matching for sensor displacement estimation

Authors:
J. Minguez;L. Montesano;F. Lamiraux
Affiliations:
Departamento de Informatica e Ingenieria de Sistemas, Zaragoza Univ.;-;-
Venue:
IEEE Transactions on Robotics
Year:
2006

Citing 0
Cited 10

Fast Laser Scan Matching using Polar Coordinates

International Journal of Robotics Research
Modeling dynamic scenarios for local sensor-based motion planning

Autonomous Robots
A Robust Iterative Closest Point Algorithm with Augmented Features

MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
On the use of likelihood fields to perform sonar scan matching localization

Autonomous Robots
An insightful comparison between experiments in mobile robotics and in science

Autonomous Robots
Automation of industrial vehicles: a vision-based line tracking application

ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Scaling iterative closest point algorithm for registration of m-D point sets

Journal of Visual Communication and Image Representation
Robust laser scan matching in dynamic environments

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Robust and accurate genetic scan matching algorithm for robotic navigation

ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Real time egomotion of a nonholonomic vehicle using LIDAR measurements

Journal of Field Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the scan matching problem for mobile robot displacement estimation. The contribution is a new metric distance and all the tools necessary to be used within the iterative closest point framework. The metric distance is defined in the configuration space of the sensor, and takes into account both translation and rotation error of the sensor. The new scan matching technique ameliorates previous methods in terms of robustness, precision, convergence, and computational load. Furthermore, it has been extensively tested to validate and compare this technique with existing methods