Precise Correntropybased 3D Object Modelling With Geometrical Traffic Prior 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 correntropybased 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 vehiclexe2x80x99s motion from arbitrary 3D motion to physically feasible 2D motion. To deal with the noise and outliers, a correntropybased multiframe matching (CorrMM) algorithm is proposed which has a robust objective function with respect to pointtoplane residual error. An efficient solver inspired by Mestimator and retraction technique on Lie group is developed, which elegantly converts the optimization of highly nonlinear 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 stateoftheart algorithms on a challenging real traffic dataset.

On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud Ground surface detection in point cloud is widely used as a key module in autonomous driving systems. Different from previous approaches which are mostly developed for lidars with high beam resolution, e.g. Velodyne HDL64, this paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution, e.g. Velodyne VLP16. The approach is based on the RANSAC scheme of plane fitting. Inlier verification for plane hypotheses is enhanced by exploiting the pointwise tangent, which is a local feature available to compute regardless of the density of lidar beams. Ground surface which is not perfectly planar is fitted by multiple (specifically 4 in our implementation) disjoint plane regions. By assuming these plane regions to be rectanglar and exploiting the integral image technique, our approach approximately finds the optimal region partition and plane hypotheses under the RANSAC scheme with realtime computational complexity.