Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera Through Relative Orientation Estimation and 1-DoF ICP

Abstract

Pose estimation and mapping are key capabilities of most autonomous vehicles and thus a number of localization and SLAM algorithms have been developed in the past. Autonomous robots and cars are typically equipped with multiple sensors. Often, the sensor suite includes a camera and a laser range finder. In this paper, we consider the problem of incremental ego-motion estimation, using both, a monocular camera and a laser range finder jointly. We propose a new algorithm, that exploits the advantages of both sensors-the ability of cameras to determine orientations well and the ability of laser range finders to estimate the scale and to directly obtain 3D point clouds. Our approach estimates the 5 degrees of freedom relative orientation from image pairs through feature point correspondences and formulates the remaining scale estimation as a new variant of the iterative closest point problem with only one degree of freedom. We furthermore exploit the camera information in a new way to constrain the data association between laser point clouds. The experiments presented in this paper suggest that our approach is able to accurately estimate the ego-motion of a vehicle and that we obtain more accurate frame-to-frame alignments than with one sensor modality alone.