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Process · 3D Point Cloud
"... Abstract Ground segmentation is a key component for Autonomous Land Ve-hicle (ALV) navigation in an outdoor environment. This paper presents a novel algorithm for real-time segmenting three-dimensional scans of various terrains. An individual terrain scan is represented as a circular polar grid map ..."
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Abstract Ground segmentation is a key component for Autonomous Land Ve-hicle (ALV) navigation in an outdoor environment. This paper presents a novel algorithm for real-time segmenting three-dimensional scans of various terrains. An individual terrain scan is represented as a circular polar grid map that is divided into a number of segments. A one-dimensional Gaussian Process (GP) regression with a non-stationary covariance function is used to distinguish the ground points or obstacles in each segment. The proposed approach splits a large-scale ground segmentation problem into many simple GP regression problems with lower com-plexity, and can then get a real-time performance while yielding acceptable ground segmentation results. In order to verify the eectiveness of our approach, experi-ments have been carried out both on a public dataset and the data collected by our own ALV in dierent outdoor scenes. Our approach has been compared with two previous ground segmentation techniques. The results show that our approach can get a better trade-o between computational time and accuracy. Thus, it can lead to successive object classication and local path planning in real time. Our approach has been successfully applied to our ALV, which won the championship in the 2011 Chinese Future Challenge in the city of Ordos.