Low-cost vector map assisted navigation strategy for autonomous vehicle

Li, Wenda; Meng, Xianjie; Zheng, Wenjun; Fang, Wenqi; Zou, Jie; Li, Huiyun; Sun, Tianfu; Liang, Jianing

doi:10.1109/apccas.2018.8605635

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…MLS was applied to the railway surveying by Tulloch Engineering in Toronto in 2015, which used to collect data at night, taking only a small part of the time required to measure with conventional ground crew and reducing the safety risk associated with on track field surveys [6]. The related studies verify the feasibility of MLS measurement for railway transportation.First, the MLS dataset contains a tremendous amount of points with geometric, colorimetric, and radiometric attributes, requiring a plethora of calculation [7,8]. Second, the point clouds are essentially a set of discrete data records and is inherently without semantic information [9].…”

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An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning

et al. 2019

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The precision of railway map is becoming a significant issue for autonomous train scheduling, monitoring and maintenance, related location-based service (LBS), and further ensuring travel safety. Mobile 3D laser scanning is an efficient method for making relative high-precision railway track maps, particularly during the night period of railway maintenance, for light detection and ranging (LiDAR) can work without ambient light. In this paper, we propose an efficient and accurate railway track vectorization method based on the LiDAR point clouds from the self-built train Mobile Laser Scanning (MLS) system. Our method takes full use of railway track geometry and reflection intensity feature of LiDAR, without any trajectory prior information. Firstly, clear track points are filtered by intensity; then, a K-means clustering fused Region-Grow Fitting algorithm is applied. It can not only extract the line vector of railway track, but also can tell the track branches apart, especially on bends and turnout. Experiments were carried on using point clouds with an average density of 490 points per square meter. The experimental results show that the method not only can quickly extract linear objects such as railway track and catenary, but also can detect the railways even in complex real-world topologies such as at bends and turnouts. The precision of the detection area in bends and turnouts are 90.32% and 81.31% respectively, the sensitivity is 83.27% and 83.33%, respectively. Moreover, it can identify the track networks.

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confidence: 99%

“…First, the MLS dataset contains a tremendous amount of points with geometric, colorimetric, and radiometric attributes, requiring a plethora of calculation [7,8]. Second, the point clouds are essentially a set of discrete data records and is inherently without semantic information [9].…”

mentioning

confidence: 99%