Precise Correntropy-based 3D Object Modelling With Geometrical Traffic Prior

Abstract

Robust 3D perception using LiDAR is of prime importance for robotics, and its fundamental core lies in precise object modelling resisting to noise and outliers. In this paper, a precise 3D object modelling algorithm is designed especially for the intelligent vehicles. The proposed algorithm is advantageous by leveraging the crucial traffic geometrical prior of road surface profile, and both the noise and outliers are elegantly handled by robust correntropy-based metric. More specifically, the road surface correction (RSC) method transforms each individual LiDAR measurement from its locally planar road surface to a globally ideal plane. This procedure essentially guarantees the reduction of vehicle's motion from arbitrary 3D motion to physically feasible 2D motion. To deal with the noise and outliers, a correntropy-based multi-frame matching (CorrMM) algorithm is proposed which has a robust objective function with respect to point-to-plane residual error. An efficient solver inspired by M-estimator and retraction technique on Lie group is developed, which elegantly converts the optimization of highly non-linear objective function into a simple quadratic programming (QP) problem. Extensive experimental results validate that the proposed algorithm attains more crisper 3D object models than several state-of-the-art algorithms on a challenging real traffic dataset.