Title: An efficient 3D point clouds covariance descriptor for object classification with mismatching correction algorithm
Authors: Heng Zhang; Bin Zhuang
Addresses: School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ' School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
Abstract: We introduce a new covariance descriptor combining object visual (colour, gradient, depth, etc.) and geometric information (3D coordinates, normal vectors, Gaussian curvature, etc.) for mobile robot with RGB-D camera to deal with point cloud data. The improved mismatching correction algorithm is applied in the feature point mismatching correction of 3D point clouds. This descriptor is able to quickly match the feature points of the point clouds in the surrounding environment and realise the function of object classification. Experimental results show that this descriptor has the advantages of compactness and flexibility compared with the previous descriptor, and greatly reduces the storage space required. At the same time, the instance and category recognition accuracy of the proposed descriptor for objects can respectively reach 94.6% and 86.8%, which are higher than those of the previous methods for object recognition of 3D point clouds.
Keywords: object classification; point clouds; covariance descriptor; mismatching correction.
International Journal of High Performance Computing and Networking, 2019 Vol.14 No.3, pp.356 - 364
Received: 31 Jul 2017
Accepted: 24 Jan 2018
Published online: 09 Sep 2019 *