Abstract
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.
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Miller, I., Campbell, M. (2007). A Mixture-Model Based Algorithm for Real-Time Terrain Estimation. In: Buehler, M., Iagnemma, K., Singh, S. (eds) The 2005 DARPA Grand Challenge. Springer Tracts in Advanced Robotics, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73429-1_13
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