Alignment of 3D point clouds with a dominant ground plane

摘要

This paper reports on a novel two-step algorithm for the estimation of full 6-degree-of-freedom (DOF) [tx, ty, tz, θx, θy, θz] rigid body transformation between any two overlapping point-clouds that have a dominant ground plane. We first estimate the ground plane (X-Y plane) from the two 3D point-clouds and align them to obtain a good estimate of the distance between the ground planes (i.e. tz) and rotations θx and θy about the X and Y axis respectively using the Rodrigues rotation formula. The remaining parameters (tx, ty, θz) are then estimated by maximizing the total mutual information (MI) between the 2D feature maps generated from the multi-modal sensor data. Experimental results using scans obtained by a vehicle equipped with a 3D laser scanner and an omnidirectional camera are used to validate the robustness of the proposed algorithm over a wide range of initial conditions. The proposed method provides an efficient framework for multi-modal sensor data fusion and provides a robust solution to the scan alignment problem.