基于车辆参考的低信道路边激光雷达点云注册

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ciyun Lin;Shuangjie Deng;Bowen Gong;Hongchao Liu
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引用次数: 0

摘要

光探测和测距(LiDAR)传感器在高分辨率、车道水平交通目标识别和轨迹跟踪方面显示出优势。它通过通知车辆、行人和骑自行车的人危险行为和潜在碰撞,赋予道路使用者权力。然而,在单路激光雷达检测中,遮挡和连续跟踪仍然是一个重大挑战。解决这个问题的一个有希望的解决方案是在道路的不同位置部署路边激光雷达传感器。在这种情况下,点云配准成为处理点云数据的关键任务。本文提出了一种新的低通道路边激光雷达点云配准方法,以移动车辆为参考点。首先,引入地面法向量旋转点云,实现z轴高度对齐;其次,提取运动车辆作为参考点;为了提高参考点的识别精度,提出了一种二维参考点校正算法。第三,提出了一种采用二维参考点进行部分配准的两步配准框架。采用迭代最近点(ICP)算法进行精确的全局配准。使用从不同交通场景收集的点云数据进行实验评估,以评估所提出的方法。结果表明,与以前的点云配准算法相比,数据配准精度有显着提高。相对旋转误差(RRE)和相对平移误差(RTE)分别小于1°和0.5 m。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Reference-Based Point Cloud Registration for Low-Channel Roadside LiDARs
The light detection and ranging (LiDAR) sensor has demonstrated its preponderance in high-resolution, lane-level traffic object identification, and trajectory tracking. It empowers road users, such as vehicles, pedestrians, and cyclists, by notifying them of dangerous behaviors and potential collisions. However, occlusion and consecutive tracking remain significant challenges in single roadside LiDAR detection. One promising solution to address this problem involves deploying roadside LiDAR sensors at different locations along the road. In such scenarios, point cloud registration becomes a crucial task in processing point cloud data. This article presents a novel point cloud registration method for low-channel roadside LiDAR, utilizing the moving vehicle as the reference points. First, ground normal vectors were introduced to rotate point clouds and achieve z-axis height alignment. Second, the motion vehicle was extracted and employed as the reference point. A 2-D reference point correction algorithm was developed to enhance the identification accuracy of the reference point. Third, a two-step registration framework was proposed, using the 2-D reference points for partial registration. The iterative closest point (ICP) algorithm was employed for accurate global registration. Experimental evaluations are conducted using point cloud data collected from diverse traffic scenarios to assess the proposed method. The results demonstrated significant improvements in data registration accuracy compared to previous point cloud registration algorithms. The relative rotation error (RRE) and relative translation error (RTE) were less than 1° and 0.5 m, respectively.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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