Scene flow for 3D laser scanner and camera system

Abstract

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.