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Title: Bayesian Generalized Kernel Inference for Terrain Traversability Mapping
We propose a new approach for traversability mapping with sparse lidar scans collected by ground vehicles, which leverages probabilistic inference to build descriptive terrain maps. Enabled by recent developments in sparse kernels, Bayesian generalized kernel inference is applied sequentially to the related problems of terrain elevation and traversability inference. The first inference step allows sparse data to support descriptive terrain modeling, and the second inference step relieves the burden typically associated with traversability computation. We explore the capabilities of the approach over a variety of data and terrain, demonstrating its suitability for online use in real-world applications.  more » « less
Award ID(s):
1723996
NSF-PAR ID:
10113014
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 2nd Conference on Robot Learning
Page Range / eLocation ID:
829 - 838
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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