Probabilistic estimation of multi-level terrain maps
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Probabilistic multi-level maps from LIDAR data
International Journal of Robotics Research
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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.