LIDAR Graph SLAM based Autonomous Vehicle Maps using XY and Yaw Dead-Reckoning Measurements

A robust Graph Slam (GS) framework for generating precise LIDAR maps is still a challenging demand for autonomous vehicle players. This is because of the sparsity of LIDAR 3D point clouds that leads to wrong compensations of relative position errors in x, y and yaw directions. We handle this drawback by proposing a unique GS strategy to process the relative position errors in the image domain. This is achieved by converting the vehicle trajectories into 2D grayscale images using “node strategy” to encode dense environmental representations with proper identifications in Absolute Coordinate System (ACS). Accordingly, Phase Correlation and Fourier Mellin Transform are employed to non-iteratively estimate the relative positions between nodes in the image domain at loop closure areas. These estimations are assigned into a set of edges that constitute the relationships between nodes in the map. Two individual cost functions are designed to utilize these edges in optimizing the nodes' yaw angles and then x, y positions in ACS. The proposed GS framework has been tested in a challenging environment of high buildings, dense trees and longitudinal bridge in Tokyo. The experimental results have verified the robustness to generate precise maps based on Dead Reckoning measurements and outperform GNSS/INS-RTK map in critical road structures.