Open AccessRobust Navigation and Mapping Architecture for Large Environments
Author(s) -
Masson Favio,
Guivant Jose,
Nebot Eduardo
Publication year - 2003
Publication title -
journal of robotic systems
Language(s) - English
Resource type - Journals
eISSN - 1097-4563
pISSN - 0741-2223
DOI - 10.1002/rob.10112
Subject(s) - simultaneous localization and mapping , extended kalman filter , convergence (economics) , kalman filter , computer science , monte carlo localization , monte carlo method , process (computing) , data association , robot , architecture , filter (signal processing) , artificial intelligence , robotics , computer vision , real time computing , algorithm , particle filter , mobile robot , mathematics , statistics , operating system , economics , visual arts , economic growth , art
This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re‐localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc.