Planar Features and 6D-SLAM based on Linear Regression Kalman Filters with n-Dimensional Approximated Gaussians

Abstract

In this paper, a six-dimensional (6D) Simultaneous Localization and Mapping (SLAM) based on novel Linear Regression Kalman Filter (LRKF), called Smart Sampling Kalman Filter (S2KF), is proposed. While the conventional feature based SLAM methods use point features as landmarks, only a few take the advantage of geometric information like corners, edges, and planes. A feature based SLAM method using planar landmarks extracted from 3D Light Detection and Ranging (LiDAR) outdoor data is proposed. The method uses the LFKF with n-dimensional approximated Gaussians by addressing the data association problem based on semantic data of plane-features. Experimental results show the appropriateness of the approach, and the filter performance is compared with the traditional filters, such as Unscented Kalman Filters and Cubature Kalman Filters.