Authors:
Martin Dimitrievski
;
David Van Hamme
;
Peter Veelaert
and
Wilfried Philips
Affiliation:
Ghent University, Belgium
Keyword(s):
Odometry, SLAM, Occupancy Map, Registration, 3DOF, LIDAR.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Registration
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
In this paper we propose a novel real-time method for SLAM in autonomous vehicles. The environment is mapped using a probabilistic occupancy map model and EGO motion is estimated within the same environment by using a feedback loop. Thus, we simplify the pose estimation from 6 to 3 degrees of freedom which greatly impacts the robustness and accuracy of the system. Input data is provided via a rotating laser scanner as 3D measurements of the current environment which are projected on the ground plane. The local ground plane is estimated in real-time from the actual point cloud data using a robust plane fitting scheme based on the RANSAC principle. Then the computed occupancy map is registered against the previous map using phase correlation in order to estimate the translation and rotation of the vehicle. Experimental results demonstrate that the method produces high quality occupancy maps and the measured translation and rotation errors of the trajectories are lower compared to other
6DOF methods. The entire SLAM system runs on a mid-range GPU and keeps up with the data from the sensor which enables more computational power for the other tasks of the autonomous vehicle.
(More)