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A mixture‐model based algorithm for real‐time terrain estimation
Author(s) -
Miller Isaac,
Campbell Mark
Publication year - 2006
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.20146
Subject(s) - terrain , elevation (ballistics) , raised relief map , algorithm , probabilistic logic , gaussian , computer science , gaussian process , remote sensing , mathematics , artificial intelligence , geography , geometry , cartography , quantum mechanics , physics
A real‐time terrain mapping and estimation algorithm using Gaussian sum elevation densities to model terrain variations in a planar gridded elevation model is presented. A formal probabilistic analysis of each individual sensor measurement allows the modeling of multiple sources of error in a rigorous manner. Measurements are associated to multiple locations in the elevation model using a Gaussian sum conditional density to account for uncertainty in measured elevation as well as uncertainty in the in‐plane location of the measurement. The approach is constructed such that terrain estimates and estimation error statistics can be constructed in real‐time without maintaining a history of sensor measurements. The algorithm is validated experimentally on the 2005 Cornell University DARPA Grand Challenge ground vehicle, demonstrating accurate and computationally feasible elevation estimates on dense terrain models, as well as estimates of the errors in the terrain model. © 2006 Wiley Periodicals, Inc.