Incremental construction of the robot's environmental map using interval analysis

Authors:
Cyril Drocourt;Laurent Delahoche;Eric Brassart;Bruno Marhic;Arnaud Clérentin
Affiliations:
UPJV – IUT Département Informatique, AMIENS Cedex 1;UPJV – IUT Département Informatique, AMIENS Cedex 1;UPJV – IUT Département Informatique, AMIENS Cedex 1;UPJV – IUT Département Informatique, AMIENS Cedex 1;UPJV – IUT Département Informatique, AMIENS Cedex 1
Venue:
COCOS'03 Proceedings of the Second international conference on Global Optimization and Constraint Satisfaction
Year:
2003

Citing 1
Cited 5

The transferable belief model

Artificial Intelligence

Brief paper: Robust set-membership state estimation; application to underwater robotics

Automatica (Journal of IFAC)
A nonlinear set membership approach for the localization and map building of underwater robots

IEEE Transactions on Robotics
Interval Analysis for Certified Numerical Solution of Problems in Robotics

International Journal of Applied Mathematics and Computer Science - Verified Methods: Applications in Medicine and Engineering
Localization of an underwater robot using interval constraint propagation

CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Loop detection of mobile robots using interval analysis

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with an original simultaneous localisation and map building paradigm (SLAM) based on the one hand on the use of an omnidirectional stereoscopic vision system and on the other hand on an interval analysis formalism for the state estimation. The first part of our study is linked to the problem of building the sensorial model. The second part is devoted to exploiting this sensorial model to localise the robot in the sense of interval analysis. The third part introduces the problem of map updating and deals with the matching problem of the stereo sensorial model with an environment map, (integrating all the previous primitive observations). The SLAM algorithm was tested on several large and structured environments and some experimental results will be presented.