AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM

Overview of the proposed AGPC-SLAM; the input is the 3D point cloud from 3D lidar. The outputs include the points map and pose estimation of vehicular trajectory.

Ground Plane Detection Using RANSAC

Illustration of ground detection using Algorithm 1 (the white points represent the raw point cloud from 3D lidar and the red points denote the detected ground point cloud).

Graph structure of the proposed AGPC-SLAM

(a) Data collection vehicle with all the sensors installed in a compact sensor kit; (b) tested scenarios; and (c) trajectory with a driving distance of 4.2 km

Illustration of the map generated by the two methods; the color of the map is annotated by the value of the z-axis (height) of each point.

Details of the generated map with reflectivity using the proposed AGPC-SLAM in Location 1

(a) Environmental condition at the start point (blue shaded circle); (b) the scene with partial ramp road (red shaded area)

Trajectories of the LeGO-LOAM (red curve), AGPC-SLAM (blue curve), and ground truth trajectory (black curve)

Altitude of the LeGO-LOAM (red curve), AGPC-SLAM (blue curve), and ground truth trajectory (black curve) during the testing area with the ramp road

Relative translation and rotation errors for LeGO-LOAM and the proposed AGPC-SLAM, respectively

Errors of the roll, pitch, and yaw angles for LeGO-LOAM and the proposed AGPC-SLAM (the x-axis denotes the ID of the GCPs from 1 to 6 and the y-axis denotes the errors)