Open AccessScene flow for 3D laser scanner and camera system
Author(s) -
Zou Cheng,
He Bingwei,
Zhang Liwei,
Zhang Jianwei
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0876
Subject(s) - computer vision , artificial intelligence , scanner , laser scanning , point cloud , computer science , optical flow , structure from motion , laser , motion field , field of view , motion estimation , point (geometry) , frame (networking) , motion (physics) , image (mathematics) , mathematics , optics , physics , telecommunications , geometry
Estimating the three‐dimensional (3D) motion from sparse laser point clouds is a highly challenging endeavour facing computer and robotic vision engineers. In this study, a novel method is proposed for robustly estimating the scene flow from a laser scanner assisted by a camera. Conditional random field (CRF) is constructed by a spatial structure of point clouds, the energy of which is minimised by a synchronous calibrated image. With the high frame rate of a laser scanner, the authors’ method allows for estimating the potential motion field as the CRF label. The authors ran an experiment on a public dataset to demonstrate that their method can accurately estimate rigid motion in outdoor scenes. They also tested the method on a laser scanner and omni‐directional camera system to find that it also accurately estimates the rigid and semi‐rigid motion of objects in a controlled indoor environment.