Vision based real-time pose estimation for intelligent vehicles

Abstract

Pose estimation is one of the key issues in the research of intelligent vehicles. In this paper, a real-time pose estimation algorithm based on vision is proposed and implemented. The ground plane assumption is used to simplify the interframe motion model to a 2D plane motion model, which reduces the computation and avoids the difficulty in feature point selection in outdoor environments. This algorithm is composed of two parts: the Gradient Angle Histogram algorithm and the Iterative Gradient Closest Point algorithm. The fusion of these two algorithms successfully addresses the local minimum problem and the high computation problem with the ICP algorithm. Experimental results with both synthetic data and real data prove the high accuracy, low computation, and high robustness to outliers in this algorithm.