面向轻轨的高精度实时视觉定位方法

王婷娴; 贾克斌; 姚萌

doi:10.16383/j.aas.c200009

面向轻轨的高精度实时视觉定位方法

doi: 10.16383/j.aas.c200009

王婷娴^{1, 2, 3,},
贾克斌^{1, 2, 3,},
姚萌^1,

1.
北京工业大学信息学部北京 100124
2.
先进信息网络北京实验室北京 100124
3.
计算智能与智能系统北京市重点实验室北京工业大学北京 100124

基金项目: 国家重点研发计划(2018YFF01010100), 国家自然科学基金(61672064), 青海省基础研究计划(2020-ZJ-709)资助

详细信息

作者简介:
王婷娴：北京工业大学信息学部硕士研究生. 主要研究方向为图像处理与视觉信息定位.E-mail: wangtingxian@emails.bjut.edu.cn

贾克斌：博士, 北京工业大学信息学部教授. 主要研究方向为图像/视频信号与信息处理. 本文通信作者.E-mail: kebinj@bjut.edu.cn

姚萌：工程师. 2018年获得北京工业大学信息学部博士学位. 主要研究方向为图像处理与视觉信息定位.E-mail: yaomeng@emails.bjut.edu.cn

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出版历程
- 收稿日期: 2020-01-08
- 录用日期: 2020-05-28
- 网络出版日期: 2021-10-13
- 刊出日期: 2021-10-13

Real-time Visual Localization Method for Light-rail With High Accuracy

WANG Ting-Xian^{1, 2, 3
,},
JIA Ke-Bin^{1, 2, 3
,},
YAO Meng^1
,

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Laboratory of Advanced Information Networks, Beijing 100124
3.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124

Funds: Supported by National Key Research and Developmet Program of China (2018YFF01010100), National Natural Science Foundation of China (61672064), and Basic Research Program of Qinghai Province (2020-ZJ-709)

More Information

Author Bio:
WANG Ting-Xian　Master student at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers image processing and visual information localization

JIA Ke-Bin　Ph.D., professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers image/video signal and information processing. Corresponding author of this paper

YAO Meng　Received his Ph.D. degree from the Faculty of Information Technology, Beijing University of Technology in 2018. His research interest covers image processing and visual information localization

摘要

摘要: 轻轨作为城市公共交通系统的重要组成部分, 对其实现智能化的管理势在必行. 针对城市轻轨定位系统要求精度高、实时强且易于安装等特点, 本文提出一种基于全局−局部场景特征与关键帧检索的定位方法. 该方法在语义信息的指导下, 从单目相机获取的参考帧中提取区别性高的区域作为关键区域. 并结合像素点位置线索利用无监督学习的方式筛选关键区域中描述力强的像素对生成二值化特征提取模式, 不仅能够提升匹配精度还显著提高了在线模块场景特征提取与匹配的速度. 其次, 以场景显著性分数为依据获取的关键帧避免了具有相似外观的场景给定位带来的干扰, 并能辅助提高场景在线匹配的精度与效率. 本文使用公开测试数据集以及具有挑战性的轻轨数据集进行测试. 实验结果表明, 本系统在满足实时性要求的同时, 其定位准确率均可达到90%以上.
- 视觉定位 /
- 位置识别 /
- 关键帧检索 /
- 关键区域检测 /
- 序列匹配
Abstract: As an important part of the urban public transportation system, it is imperative to realize the intelligent management of light rail. By considering the practical requirements like high accuracy, real-time performance, and easy installation, this paper proposes a visual localization method based on global-local features and keyframe retrieval. Under the guidance of semantic information, the region with high significance in each reference frame obtained by the monocular camera is extracted as the key region. Combined with the location cues of pixels, unsupervised learning is used to filter the pixel pairs with strong description force in the key region to generate the binary pattern, which greatly reduces the computation of feature extraction and matching in the online module while improving the matching accuracy. Secondly, the keyframes obtained based on the discrimination score can effectively avoid the interference caused by the scene with analogous appearance, and assist to improve the accuracy and efficiency of online scene matching. The Nordland dataset and the challenging light rail dataset are used for testing. The experimental results show that the precision of the system can reach more than 90% while meeting real-time requirements.
- Visual localization /
- place recognition /
- keyframe retrieval /
- key region detection /
- sequence matching

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图 1 所提轻轨定位系统框架

Fig. 1 The framework of our proposed light-rail localization system

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图 2 计算像素显著性分数的示意图

Fig. 2 Illustration of computing saliency score of pixel

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图 3 关键帧在参考序列中的分布

Fig. 3 Distribution of keyframes in the reference sequence

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图 4 关键区域检测结果

Fig. 4 The result of key region detection

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图 5 在线场景序列匹配中的关键帧检索窗口

Fig. 5 Illustration of the keyframe retrieval window for online sequence

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图 6 中国香港轻轨数据集中复杂多变的场景示例

Fig. 6 Examples of complex and volatile scenes in the China Hong Kong light-rail dataset

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图 7 不同语义分割网络获得的结果示例

Fig. 7 Example results of different semantic segmentation network

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图 8 不同方法的匹配偏差和计算时间

Fig. 8 Matching offset and computation time of different methods

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图 9 系数K对匹配精度的影响

Fig. 9 The influence of coefficient K for matching accuracy

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图 10 不同方法在单帧场景识别中的性能表现

Fig. 10 Performance of different methods in single frame scene recognition

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图 11 本文方法在MTRHK数据集中的匹配结果

Fig. 11 Illustration of matching results from the MTRHK dataset

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表 1 Nordland和MTRHK数据集中所需参数设置

Table 1 Parameter settings for Norland and MTRHK datasets

参数符号	参数定义	参数值 (Nordland)	参数值 (MTRHK)
V_min	最小路径拟合速度	0.8	0
V_max	最大路径拟合速度	1.2	1.5
V_step	路径拟合速度步长	0.1	0.1
N_c	像素对提取个数	512	512
K	关键区域检测系数	1.05	1.05
T_L	最佳匹配距离阈值	175	175

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表 2 不同语义分割模型间的精度对比

Table 2 Accuracy comparison of different semantic segmentation network

语义分割网络	平均交并比 (%)
语义分割网络	Nordland	MTRHK
FCN	67.9	54.9
PSPNet	70.8	32.7
Deeplab	71.7	55.8
RefineNet	72.5	59.2
DFN	73.0	48.2
BiSeNet	72.2	36.2
融合模型	78.0	64.6

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表 3 不同方法对每帧图像的平均描述时间对比(s)

Table 3 Comparison of average describing time for each image by different methods (s)

方法	SeqSLAM	LRT-ULL	本文方法
时间	0.1327	1.2791	0.1238

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表 4 不同场景跟踪算法的准确率(%)与召回率(%)

Table 4 Precision (%) and recall (%) of different scene tracking methods

数据集	准确率 (召回率)
数据集	SeqSLAM	SeqCNNSLAM	本文方法
Nordland	89.56 (100)	99.67 (100)	99.24 (100)
MTRHK	39.71 (100)	60.72 (100)	90.20 (100)

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表 5 在Nordland数据集和MTRHK数据集中不同场景跟踪算法的消耗时间(s)

Table 5 The consumption time of different scene tracking methods in the Nordland and the MTRHK dataset (s)

数据集	平均消耗时间
数据集	SeqSLAM	SeqCNNSLAM	本文方法
Nordland	0.67×10⁻¹	6.51×10⁻³	3.17×10⁻³
MTRHK	0.50×10⁻¹	4.90×10⁻³	2.37×10⁻³

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