Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras

Title
Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras
Authors
선우명호
Keywords
Precise localization; multiple cameras; road surface marker; probabilistic noise modeling; probabilistic noise model of road surface marker (RSM) features; particle filtering; autonomous car; autonomous driving
Issue Date
2015-12
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v. 16, NO 6, Page. 3377-3392
Abstract
This paper presents a Monte Carlo localization algorithm for an autonomous car based on an integration of multiple sensors data. The sensor system is composed of onboard motion sensors, a low-cost GPS receiver, a precise digital map, and multiple cameras. Data from the onboard motion sensors, such as yaw rate and wheel speeds, are used to predict the vehicle motion, and the GPS receiver is applied to establish the validation boundary of the ego-vehicle position. The digital map contains location information at the centimeter level about road surface markers (RSMs), such as lane markers, stop lines, and traffic sign markers. The multiple images from the front and rear mono-cameras and the around-view monitoring system are used to detect the RSM features. The localization algorithm updates the measurements by matching the RSM features from the cameras to the digital map based on a particle filter. Because the particle filter updates the measurements based on a probabilistic sensor model, the exact probabilistic modeling of sensor noise is a key factor to enhance the localization performance. To design the probabilistic noise model of the RSM features more explicitly, we analyze the results of the RSM feature detection for various real driving conditions. The proposed localization algorithm is verified and evaluated through experiments under various test scenarios and configurations. From the experimental results, we conclude that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications.
URI
http://ieeexplore.ieee.org/document/7160754/http://hdl.handle.net/20.500.11754/29539
ISSN
1524-9050; 1558-0016
DOI
http://dx.doi.org/10.1109/TITS.2015.2450738
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > DEPARTMENT OF AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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