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Vehicle localization with low cost radar sensors
KTH, School of Computer Science and Communication (CSC). (CVAP)
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS/RPL/CSC)
2016 (English)In: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
Abstract [en]

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016.
Keyword [en]
Sensors, Iterative closest point algorithm, Roads, Vehicles, Spaceborne radar, Laser radar
National Category
Research subject
Computer Science
URN: urn:nbn:se:kth:diva-192860DOI: 10.1109/IVS.2016.7535489ISBN: 978-1-5090-1821-5OAI: diva2:972553
Intelligent Vehicles Symposium (IV), 2016 IEEE
VINNOVA, 2013-03964

QC 20160929

Available from: 2016-09-21 Created: 2016-09-21 Last updated: 2016-10-05Bibliographically approved

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