TY - JOUR
T1 - Self-localization based on visual lane marking maps
T2 - an accurate low-cost approach for autonomous driving
AU - Vivacqua, Rafael Peixoto Derenzi
AU - Bertozzi, Massimo
AU - Cerri, Pietro
AU - Martins, Felipe Nascimento
AU - Vassallo, Raquel Frizera
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
AB - Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
KW - autonomous driving
KW - computer vision
KW - dead reckoning
KW - lane marking detectors
KW - map matching
KW - mapping and localization
KW - zelfsturend verkeer
UR - https://www.mendeley.com/catalogue/11190bcb-6e6c-3ecd-9f29-c2223b9936d2/
U2 - 10.1109/tits.2017.2752461
DO - 10.1109/tits.2017.2752461
M3 - Article
SN - 1524-9050
VL - 19
SP - 582
EP - 597
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 2
M1 - 2
ER -