Department of Electronic Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju-si 27469, Korea.
School of Intelligent Mechatronics Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
Sensors (Basel). 2018 Oct 22;18(10):3590. doi: 10.3390/s18103590.
In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.
为了克服 GNSS/INS 的局限性，并保持大规模生产车辆的成本负担得起，正在开发一种融合低成本 GNSS/INS 和低成本感知传感器估算车辆位置的精确定位系统。对于车辆位置估计，感知传感器检测道路设施并将其用作地标。对于这种定位系统，本文提出了一种使用成本相对较低的单目相机检测路标作为地标物的方法。由于路标内部图案和纵横比各不相同，因此所提出的方法基于基于部分的方法，该方法检测角并将它们组合以检测路标。虽然基于卷积神经网络的现有检测器的召回率、精度和处理时间分别为 99.63%、98.16%和 4802ms，但所提出方法的召回率、精度和处理时间分别为 97.48%、98.78%和 66.7ms。所提出方法的检测性能与现有检测器一样好，并且其处理时间大大减少，适用于嵌入式系统。
