人工智能
计算机视觉
稳健性(进化)
计算机科学
单眼
地标
同时定位和映射
匹配(统计)
帧(网络)
地图匹配
比例(比率)
模式识别(心理学)
数学
移动机器人
地理
地图学
机器人
电信
生物化学
化学
统计
全球定位系统
基因
作者
Tuopu Wen,Kun Jiang,Benny Wijaya,Hangyu Li,Mengmeng Yang,Diange Yang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-11-01
卷期号:23 (11): 20268-20281
被引量:5
标识
DOI:10.1109/tits.2022.3176914
摘要
Vision-based map-matching with HD map for high precision vehicle localization has gained great attention for its low-cost and ease of deployment. However, its localization performance is still unsatisfactory in accuracy and robustness in numerous real applications due to the sparsity and noise of the perceived HD map landmarks. This article proposes the tightly-coupled monocular map-matching localization algorithm (TM3Loc) for monocular-based vehicle localization. TM3Loc introduces semantic chamfer matching (SCM) to model monocular map-matching problem and combines visual features with SCM in a tightly-coupled manner. By applying the sliding window-based optimization technique, the historical visual features and HD map constraints are also introduced, such that the vehicle poses are estimated with an abundance of visual features and multi-frame HD map landmark features, rather than with single-frame HD map observations in previous works. Experiments are conducted on large scale dataset of 15 km long in total. The results show that TM3Loc is able to achieve high precision localization performance using a low-cost monocular camera, largely exceeding the performance of the previous state-of-the-art methods, thereby promoting the development of autonomous driving.
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