A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the s...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2017-10, Vol.17 (10), p.2359
Main Authors: Vivacqua, Rafael, Vassallo, Raquel, Martins, Felipe
Format: Article
Language:English
Subjects:
autonomous driving
computer vision
data fusion
Data integration
Dead reckoning
Driving
ego-localization
inertial navigation system
lane marking detector
Localization
Low cost
map matching
Multisensor fusion
Navigation satellites
Obstacle avoidance
Sensors
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Summary:Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle's backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.
ISSN:1424-8220
1424-8220
DOI:10.3390/s17102359