Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

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Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this letter presents a novel ground segmentation method called Patchwork , which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this letter, a point cloud is encoded into a Concentric Zone Model–based representation to assign an appropriate density of cloud points among bins in a way that is not computationally complex. This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. Finally, Ground Likelihood Estimation is introduced to dramatically reduce false positives. As experimentally verified on SemanticKITTI and rough terrain datasets, our proposed method yields promising performance compared with the state-of-the-art methods, showing faster speed compared with existing plane fitting–based methods. Code is available: https://github.com/LimHyungTae/patchwork
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-10
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.6, no.4, pp.6458 - 6465

ISSN
2377-3766
DOI
10.1109/LRA.2021.3093009
URI
http://hdl.handle.net/10203/287045
Appears in Collection
EE-Journal Papers(저널논문)
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