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Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels
Author(s) -
Fairfield Nathaniel,
Kantor George,
Wettergreen David
Publication year - 2007
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20165
Subject(s) - octree , simultaneous localization and mapping , particle filter , sonar , computer science , underwater , locality , filter (signal processing) , representation (politics) , grid , artificial intelligence , computer vision , exploit , real time computing , robot , geology , mobile robot , linguistics , philosophy , computer security , geodesy , politics , political science , law , oceanography
Abstract We describe a simultaneous localization and mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three‐dimensional (3D) environment. Our method consists of a Rao‐Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real‐time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure that makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacato´n in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these real‐world datasets. © 2007 Wiley Periodicals, Inc.