智能驾驶车载激光雷达关键技术与应用算法

陈晓冬, 张佳琛, 庞伟凇, 等. 智能驾驶车载激光雷达关键技术与应用算法[J]. 光电工程, 2019, 46(7): 190182. doi: 10.12086/oee.2019.190182
引用本文: 陈晓冬, 张佳琛, 庞伟凇, 等. 智能驾驶车载激光雷达关键技术与应用算法[J]. 光电工程, 2019, 46(7): 190182. doi: 10.12086/oee.2019.190182
Chen Xiaodong, Zhang Jiachen, Pang Weisong, et al. Key technology and application algorithm of intelligent driving vehicle LiDAR[J]. Opto-Electronic Engineering, 2019, 46(7): 190182. doi: 10.12086/oee.2019.190182
Citation: Chen Xiaodong, Zhang Jiachen, Pang Weisong, et al. Key technology and application algorithm of intelligent driving vehicle LiDAR[J]. Opto-Electronic Engineering, 2019, 46(7): 190182. doi: 10.12086/oee.2019.190182

智能驾驶车载激光雷达关键技术与应用算法

  • 基金项目:
    天津市科技计划基金资助项目(17ZXRGGX00140)
详细信息
    作者简介:
    通讯作者: 蔡怀宇(1965-),女,博士,教授,主要从事光电成像与检测的研究。E-mail:hycai@tju.edu.cn
  • 中图分类号: TN958.98; TH741

Key technology and application algorithm of intelligent driving vehicle LiDAR

  • Fund Project: Supported by Tianjin Science and Technology Plan Fund (17ZXRGGX00140)
More Information
  • 随着全球智能驾驶进入产业化与商业化的准备期,车载激光雷达凭借其优异性能已成为不可或缺的环境感知传感器并在硬件技术和应用算法上得到迅猛发展。文章以激光雷达扫描方式及相关技术为切入点对智能驾驶车载激光雷达硬件关键技术进行了介绍,分别讨论了机械式、混合式和全固态车载激光雷达的原理、特点及现状;以智能驾驶应用任务为导向,对点云分割、目标跟踪与识别、即时定位与地图重建这三类车载激光雷达应用算法进行了归纳总结。分析可见,车载激光雷达为降低成本、提升性能、满足智能驾驶需求将进一步走向固态化、智能化和网络化;应用算法研究的追求目标则是实时、高效和可靠。

  • Overview: With the preparation of intelligent driving into industrialization and commercialization, LiDAR has become an indispensable environmental sensor with its excellent performance and has developed rapidly in hardware technology and application algorithms. This paper introduces the key technologies of intelligent driving vehicle LiDAR hardware by using LiDAR scanning method and related technology as the entry point, discussing the principle, characteristics and current status of mechanical, hybrid and all-solid-state automotive LiDAR; three points of vehicle LiDAR application algorithms, point cloud segmentation, target tracking and recognition, real-time location and map reconstruction are summarized and analyzed. Mechanical vehicle LiDAR is the earliest LiDAR product used in intelligent driving. It is still widely used in intelligent driving test vehicles because of its simple principle, easy to drive and achieve horizontal 360° scanning. The cost and unreliability of long-term use in the driving environment hinder its promotion and popularity. The hybrid vehicle-mounted LiDAR makes the mechanical structure miniaturized and electronically designed; the main components are achieved by the chip process, which is technically easy to implement and has begun to be applied in the intelligent driving solution, but its performance still needs to be optimized. The all-solid-state on-board laser radar does not have any macro or micro moving parts inside, which is reliable and durable. However, the manufacturing process is difficult and still in the early stage of development. The purpose of the LiDAR application algorithms is to accurately and reliably sense the surrounding environment and thus ensure safe and efficient driving. Among them, the point cloud segmentation algorithm is the basis of target tracking and recognition. Target tracking and recognition will realize the judgment of the motion state and geometric features of obstacles around the car. SLAM will realize the precise positioning and passable path planning of the car. The existing automotive LiDAR application algorithms have different degrees of limitations. Firstly, the accuracy and real-time application of the algorithm are difficult to satisfy at the same time. Secondly, the algorithms are mostly developed for a specific scenario, and it is difficult to ensure portability and stability. The complexity and diversity of the scene make the research of the algorithm colorful, showing a multi-level, multi-angle and thus multi-faceted situation. The analysis shows that the vehicle LiDAR will further move toward solid-state, intelligent and networked in order to reduce costs, improve performance and meet intelligent driving requirements. While, the pursuit of application algorithm research is real-time, efficient and reliable.

  • 加载中
  • 图 1  Luminar激光雷达运行于智能驾驶车上生成的周围环境点云图[4]

    Figure 1.  Luminar LiDAR records a point cloud image its surroundings[4]

    图 2  脉冲测距式车载激光雷达系统结构示意图

    Figure 2.  Structure of the pulsed distance measurement vehicle LiDAR system

    图 3  Velodyne激光雷达。(a) HDL-64E;(b) VLS-128[12]

    Figure 3.  Velodyne LiDAR. (a) HDL-64E; (b) VLS-128[12]

    图 4  MEMS车载激光雷达发射系统结构

    Figure 4.  Structure of MEMS vehicle LiDAR transmitting system

    图 5  Quanergy OPA激光雷达S3[22]

    Figure 5.  Quanergy OPA LiDAR S3[22]

    图 6  点云目标快速检测与跟踪[42]

    Figure 6.  Point cloud target rapid detection and tracking[42]

    图 7  Cartographer所构建室内场景地图[58]

    Figure 7.  Indoor scene map constructed by Cartographer[58]

    表 1  不同材料用于光学相控阵激光扫描时的参数对比

    Table 1.  Parameters of different materials used in optical phased array laser scanning

    液晶 铌酸锂晶体 GaAs PLZT压电陶瓷 铁电畴 光纤光栅
    响应时间 ms ps ns ns ms ns
    驱动电压/V < 10 < 10 < 10 < 1000 ≈5000 700
    扫描角度/(°) < 10 < 0.1 ≈30 < 0.1 ≈10 ≈20
    下载: 导出CSV

    表 2  车载激光雷达应用算法

    Table 2.  Vehicle LiDAR application algorithm

    类别 算法名称 关键技术 特点
    点云分割 非模型投影法
    聚类法
    地面投影法、虚拟像平面投影法
    K-means聚类等
    简单高效,不适用于复杂形体分割
    分割准确性高,较复杂
    目标跟踪与识别 检测与跟踪 物体级检测与跟踪
    栅格单元级检测与跟踪
    实时性好,不适用于复杂环境
    跟踪精度高,计算效率低
    分类与识别 基于全局特征的目标分类与识别
    基于局部特征的目标分类与识别
    识别速度快,受环境遮挡影响大
    噪声不敏感,抗密度千扰性差
    即时定位与地图构建 基于滤波器的SLAM
    基于图优化的SLAM
    扩展卡尔曼滤波器、Fast SLAM等
    位姿图优化等
    较准确,不适用于大场景
    全局优化,对初始值要求高
    下载: 导出CSV
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出版历程
收稿日期:  2019-04-17
修回日期:  2019-06-06
刊出日期:  2019-07-01

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