Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles

Martin Dimitrievski, David Van Hamme, Peter Veelaert, Wilfried Philips

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.

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Paper Citation


in Harvard Style

Dimitrievski M., Van Hamme D., Veelaert P. and Philips W. (2016). Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 626-633. DOI: 10.5220/0005719006260633


in Bibtex Style

@conference{visapp16,
author={Martin Dimitrievski and David Van Hamme and Peter Veelaert and Wilfried Philips},
title={Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={626-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005719006260633},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles
SN - 978-989-758-175-5
AU - Dimitrievski M.
AU - Van Hamme D.
AU - Veelaert P.
AU - Philips W.
PY - 2016
SP - 626
EP - 633
DO - 10.5220/0005719006260633