Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud

Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data....

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Dades bibliogràfiques

Autors principals:	Victor Jimenez, Jorge Godoy, Antonio Artunedo, Jorge Villagra
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2021-01-01
Col·lecció:	IEEE Access
Matèries:	Belief propagation channel-based geometric features LiDAR Markov random field obstacle-ground segmentation
Accés en línia:	https://ieeexplore.ieee.org/document/9548034/
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