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现阶段APD三维成像激光雷达主要以脉冲激光测距[3]为基础,如图1所示,由发射系统发射一束窄脉冲照射目标,经目标反射或散射的回波经过接收透镜照射至探测器,通过统计发射与接收时刻的飞行时间获得目标距离。在测距基础上,通过扫描或面阵接收方式对物空间进行三维信息获取(强度+距离)。其中,扫描方式主要是通过机械/电子等扫描装置(例如:振镜、转镜、双光楔、MEMS、相控阵等)实现对光束偏转,从而获得角度−角度−距离像。面阵接收方式主要是通过泛光照明(即大范围闪光照明)[4]与APD阵列配合,一次性获得所有通道的距离与强度信息,因此,这种方式亦被称为闪光(FLASH)激光雷达,成像效率高,但由于大面阵APD阵列价格昂贵且远距离探测所需激光发射能量高,为了平衡面阵APD与扫描方式的各自优缺点,基于点阵+扫描的方式也得到了诸多研究与应用。下文按照APD三维成像激光雷达的主要组成部分(发射、接收、数据处理),进行单元关键技术分析。
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发射单元主要包括激光器、准直扩束系统以及扫描方法。准直扩束系统可以与激光器集成或者根据实际需求通过光学优化设计进行外置。以远距离侦察与无人驾驶应用为例,目前采用较多的为半导体激光器[5]、固体激光器[6]、光纤激光器[7]等,如图2所示。无人驾驶方面,以905 nm半导体激光器为主,原因在于成本低、体积小。远距离侦察方面,以YAG固体激光器(1 064 nm)为主,原因在于脉冲能量较高,可以探测千米以外的目标。光纤激光器具有光束质量优、功率/脉冲能量高、可柔性传输、维护简单等优点[8],尤其是配合一些新体制三维成像,例如:利用相位调制器以及相关算法可以实现光纤相控扫描与高功率合成[9],从而可完成光束的灵巧指向,或者产生高速随机光斑用于计算成像等[10]。
近年来,以1 550 nm波段为代表的垂直腔面发射激光器(Vertical Cavity Surface Emitting Laser, VCSEL)得到了广泛研究,被认为有望替代905 nm的下一代激光雷达光源,主要原因在于:一方面,1 550 nm相对905 nm穿透性更好,因此在相同峰值功率条件下可以探测更远距离,且对人眼安全性更好;另一方面,VCSEL的光学谐振腔与半导体芯片的衬底垂直,能够实现芯片表面的激光发射,有阈值电流低、稳定单波长工作、易高频调制与阵列集成等优势[11]。
扫描方法包括机械与非机械两类,机械扫描方法技术成熟,但效率较低,包括转镜、光楔、振镜等。国外典型产品包括瑞士Leica、加拿大Optech、美国Trimble、奥地利Riegl等,测距范围可达1 km,精度可达到mm量级。非机械扫描方式包括较多,包括声光[12]、电光[13]、光栅[14]、MEMS[15]、相控阵[16]等。其中,MEMS属于微机械扫描,相控阵为电子扫描,这两种方式在应用现状分析中将进一步阐述。
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APD是利用PN结在高反偏置电压下产生的雪崩效应进行工作的一种光电探测器。APD具有两种工作模式[2]:线性模式(Linear Mode)和盖革模式(Geiger Mode, GM)。线性模式是指当APD偏置电压低于其击穿电压时,对入射光产生的光电流起到线性放大作用,即电流随入射光强呈线性变化;盖革模式是指当偏置电压接近或高于雪崩电压,APD增益迅速增加,从而使得单光子诱发输出电流饱和,因此,此方式亦被称为单光子计数模式。
APD作为三维成像激光雷达核心器件,典型应用形式包括单点、点阵与面阵三种,具体采用何种方式需要同时兼顾设计需求与成本。从制造难度看,单点方式最为容易,次之是点阵,面阵最难。目前,大面阵(≥128×128) APD阵列对电子制造工艺仍然是个严峻的挑战,主要由于APD的高灵敏度使得电子噪声、探测器之间的串扰难以消除,间距越小,噪声越大。据公开资料,现阶段可实现的大面阵APD阵列为256×256[17],其分辨率远低于目前二维图像传感器。可见,难以获取大面阵APD器件导致非扫描三维成像难以满足高分辨率的成像需求。因此,国内外对面阵APD也开展了诸多研究。例如:美国Raytheon公司长期致力于高灵敏APD阵列芯片的研究工作,该公司在美国国家航空和宇宙航行局(NASA)、美国空军实验室(AFRL)、海军航空兵武器系统部(NAVAIR)等国防部门的资助下,先后研制出一系列高灵敏度APD阵列,包括单点组成阵列式APD阵列,大面阵APD阵列(256×256),还包括不同衬底材料的APD阵列,例如:硅(Si)APD阵列,碲镉汞(CdHgTe)APD阵列,其中,碲镉汞(CdHgTe) APD能够获得更高的雪崩增益,在相对较高温的状态下,仍然能够保持增益对光强的线性响应,使得系统能同时得到强度像与距离像,更有利于获得目标的更多信息。我国重庆光电技术研究所相关研究人员研制了由InGaAs/InP雪崩光电二极管阵列组成的时间计数型CMOS读出电路,先后突破了8×8、32×32阵列规格盖革模式APD阵列,在盖革模式下具有单光子探测灵敏度,时间计数型CMOS读出电路在每个单元获取光子飞行时间,实现ns级时间分辨率,并完成淬灭功能,单元时间抖动为332 ps,串扰累积概率为15.6%。哈尔滨工业大学在国内自研InGaAs材料的32×32像元GM-APD基础上,搭建了一套1 570 nm激光主动成像实验平台[18],在单脉冲能量2 mJ条件下,获得了外场3.9 km目标的轮廓像,在720 m处能获得目标的清晰表面结构距离像。西南技术物理研究所也研制32×32盖革APD阵列[19],实现了远距离三维成像。总体来看,推进大面阵APD阵列技术,有利于实现快速三维成像,提高激光雷达的集成性。
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基于激光雷达点云的算法是实现三维重构、场景理解、目标特征获取的重要研究内容。相比较传统二维成像方式,增加了距离信息的点云重构使得场景信息更为丰富,尤其是一些复杂场景下的目标获取,例如:丛林、叶簇等伪装、遮挡物下的目标获取。目前点云算法单元主要包括点云的滤波、特征提取与分割。由于点云数据中通常存在噪声点以及冗余信息,需要对噪声点云及冗余进行过滤,目前点云滤波技术主流方法包括有直通滤波、体素滤波、统计滤波和半径滤波等,如表1所示。
表 1 主流滤波效果对比
Table 1. Mainstream filter effect comparison
Filter name Principle Features Pass-through filtering For point cloud data with certain spatial characteristics in the spatial distribution, determine the range of the point cloud in the x, y, and z axis directions, and then filter the threshold to remove the different range points The speed is fast, but the filtering is not accurate enough, which is often a rough process in the filtering process Voxel filtering A voxel is a small space in three dimensions. Create a voxel grid on the input point cloud data, and then in each voxel, all existing points will be approximated by their centroids The number of point clouds is reduced without destroying the geometry of the point cloud itself Statistical filtering Perform noise filtering based on point cloud density. By calculating the average distance from each point to its nearest neighbor, the Gaussian distribution of all points in the point cloud is obtained, and then a distance threshold can be determined according to the mean and variance to filter outliers. The filtering effect is better than straight-through filtering, which can accurately filter out sparse outliers Bilateral filtering Given a threshold, calculate the number of point clouds under each radius. When the number is greater than the given threshold, keep it, otherwise filter out Can filter out internal noise more quickly than statistical filtering 点云的特征指点云数据中能够表示实体几何特性或纹理特征的点的集合。特征提取主要包括局部特征和全局特征。点云分割是将杂乱无章的点云数据分割成若干个互不相交的子集,并将每一个子集的数据给一定的语义信息。点云分割主要包含:(1) 基于边缘的分割方法:通过检测点云数据中隐藏的边缘信息得到分割区域,通常用来描述物体的形状特性。Bhanu等人[20]在1896年首先提出通过计算梯度信息,检测单位法向量的方向变化来检测边缘;KeYinglin等人[21]将点云划分为网格,检测边缘网格并且分割相应的点云。(2) 基于面积的分割算法:基于面积分割算法以点云曲面作为起点,通过相似度度量,对各个点云曲面周围的离散点云进行分组,进而将种子逐步扩展到更大的曲面[22]。(3) 基于模型的点云分割:利用原始几何形态的数学模型作为先验知识进行分割,使具有相同模型的点云数据被分割到同一区域。Fischer[23]提出随机抽样一致性算法用于检测直线,圆等特征。(4) 基于图的分割算法:利用点云数据构造图的结构进行分割,将分割问题转换为概率推理模型。WR Green等人[24]将空间、几何和外观特征结合起来作为图边缘的权重计算方法,对室内场景点云进行分割。Yang等人[25]使用图模型方法进行区域融合,通过最小化能量函数得到边界清晰的RGB-D图像分割结果。
总的来说,基于三维点云算法方面的研究从滤波、特征提取与分割方面已经取得了一定进展,并且针对简单目标的理解已经比较成熟,但是针对室外大场景下稠密激光点云的场景理解(比如在有伪装或者遮挡情况下)还有许多待解决的问题。
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三维成像分类方式多样化,若以载荷应用需求分类,可以分为星载、车载、机载、船载、弹载等方式。其中,受限于体积与成像效率,弹载三维成像激光雷达尚处于刚起步阶段,离实用化仍有较长距离。船载激光雷达已有开始应用,但目前多是将车载激光雷达移植在船载平台,此种方式对于水面上的3D重构与目标识别类似车载情况对待,而对于水下一体化探测的三维成像激光雷达还有待进一步研究。星载扫描成像激光雷达主要用于空间交会对接、飞行器导航着陆以及星载对地三维成像等。由于星载距离远(百公里),因此工作方式多以扫描为主。机载激光雷达已有较为成熟应用,例如:奥地利的Riegl、加拿大的Optech、瑞士的Leica,国内的中海达、北科天汇等产品。随着大面阵APD阵列工艺日趋完善,扫描与凝视相结合的成像方式也得到了广泛应用,在植被遮挡目标探测下,2003年,美国国防高级研究计划局(DARPA)和美国陆军夜视和电子传感器管理局(NVESD)共同研制的一种集成、轻型的用于无人机平台的Jigsaw激光三维成像雷达系统,旨在通过更高分辨率三维成像发现、识别隐藏于植被或伪装的目标。
受大数据、互联网、人工智能驱动的影响,车载激光雷达[26]是自动驾驶的研究热点,下文以车载激光雷达应用现状为例进行讨论,按照扫描与非扫描方式,国内外车载激光雷达的研究现状[27]如图3所示,可以看到,国外已研制出车规级激光雷达(例如:法雷奥),而我国尚未形成可批量应用于车载的激光雷达;从技术指标,我国与国外持平,均已有突破百线的激光雷达,既有适合放置于车顶的周视三维成像激光雷达,也有适合嵌入车身的前置成像激光雷达;从成像体制看,MEMS与相控阵被认为目前扫描式激光雷达中具有良好前景的两种成像方式,国内外均在研究。MEMS扫描[28]:国内外均有基于MEMS的高分辨三维成像研发能力,且均已突破百线以上分辨率,实时性可达到20 Hz以上。相控阵扫描:国外已研制出光波导的相控阵扫描激光雷达[29],并已有实物样机,我国尚有一定差距,因此,对于新体制的三维成像研究也是提高三维成像综合性能的必由之路。对比MEMS与相控阵两种扫描成像体制(表2),一方面,尽管相控阵无任何惯性,但因现有电子工艺技术难以达到相邻发射单元间距小于光波长之半,从而导致光束合成效率低、旁瓣栅瓣严重;另一方面,MEMS仍属于机械扫描,其出现是代替传统的机械同轴多光束扫描成像激光雷达,尽管具有惯性小、摆幅大、频响高的优点,但因振动、高低温、冲击等高强度环境的影响,器件集成仍是需要进一步解决的技术难点。
图 3 国内外车载三维成像激光雷达典型产品对比图
Figure 3. Comparison of typical products of vehicle three-dimensional imaging lidar at domestic and foreign
表 2 MEMS与OPA成像激光雷达对比
Table 2. Comparison of MEMS and OPA imaging laser radar
MEMS Phased array Core principle Micromechanical scanning Transmitter unit array + phase control Advantage Small size, light weight, large swing, small inertia Small size, light weight, no inertia, large swing (depending on unit spacing) Disadvantages Small target surface and low damage threshold Damage threshold is high, can form high power Craft difficulty Relatively easy Difficult (unit spacing is less than half of the optical wavelength) Cost Lower Reduce costs after batching Current research situation Relatively mature Immature
Research progress of APD three-dimensional imaging lidar
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摘要: 三维成像激光雷达因获取信息丰富、抗干扰能力强、分辨率高等优势已广泛应用于地貌勘测、自动驾驶、智能交通、视觉跟踪等国防与民用领域。随着雪崩光电二极管(APD)探测器件的发展与三维成像体制的多样化(例如:微机电系统扫描、相控阵、闪光等),激光雷达性能较早期已得到大幅提升。立足于军民领域对激光雷达的新需求,迫切需求新方法、新体制进一步提升三维成像的综合性能。首先从APD三维成像激光雷达的发射单元、接收单元、算法单元(数据处理单元)三方面关键技术展开分析。然后,以载荷应用需求对三维成像激光雷达进行了分类阐述与讨论,重点以车载环境感知为例深入讨论了现有激光雷达的应用现状与军民应用所面临的难点问题。基于APD器件的三维成像方法多元化发展,讨论了两种适用于APD器件的新型三维成像方法(异构变分辨率与鬼成像)。最后,在分析三维成像激光雷达研究现状的基础上,总结了三维成像激光雷达正朝着大视场、高分辨、高精度、实时性、模块化、智能化的方向发展,为进一步研究高性能三维成像激光雷达奠定基础。Abstract: Due to the advantages of rich information, strong anti-interference ability and high resolution, three-dimensional (3D) imaging lidar has been widely used in defense and civil fields, such as geomorphology surveys, autopilot, smart transportation and visual tracking. With the development of avalanche photodiode detector (APD) and the multiplicities of 3D lidar (e.g., MEMS, optical phased array, flash, etc.), the performances of lidar has been greatly improved compared with that of initial 3D systems. According to the new requirements on 3D lidar for the military and civilian fields, novel methods and mechanisms were proposed to improve comprehensive performances of 3D imaging. First of all, the three key technologies of APD-based 3D imaging lidar were analyzed, including the transmitting unit, the receiving unit, and the algorithm unit (data processing unit). Then, 3D imaging lidar was classified and discussed according to the different applications for loading. Among them, 3D imaging lidar based on unmanned vehicle was selected as the typical example for illustrating the application status and the difficulties faced with military and civilian applications. Based on the diversified development of 3D imaging methods, two novel 3D imaging methods (heterogeneous resolution and ghost imaging) suitable for APD devices were discussed. Finally, based on the analysis of the research status of 3D imaging lidar, it is concluded that 3D imaging lidar is developing towards the large field of view, high resolution, high precision, real-time, modularity and intelligence, which paves the way for developing high performances of 3D imaging lidar.
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Key words:
- three-dimensional imaging /
- lidar /
- APD /
- identification /
- subdivision
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表 1 主流滤波效果对比
Table 1. Mainstream filter effect comparison
Filter name Principle Features Pass-through filtering For point cloud data with certain spatial characteristics in the spatial distribution, determine the range of the point cloud in the x, y, and z axis directions, and then filter the threshold to remove the different range points The speed is fast, but the filtering is not accurate enough, which is often a rough process in the filtering process Voxel filtering A voxel is a small space in three dimensions. Create a voxel grid on the input point cloud data, and then in each voxel, all existing points will be approximated by their centroids The number of point clouds is reduced without destroying the geometry of the point cloud itself Statistical filtering Perform noise filtering based on point cloud density. By calculating the average distance from each point to its nearest neighbor, the Gaussian distribution of all points in the point cloud is obtained, and then a distance threshold can be determined according to the mean and variance to filter outliers. The filtering effect is better than straight-through filtering, which can accurately filter out sparse outliers Bilateral filtering Given a threshold, calculate the number of point clouds under each radius. When the number is greater than the given threshold, keep it, otherwise filter out Can filter out internal noise more quickly than statistical filtering 表 2 MEMS与OPA成像激光雷达对比
Table 2. Comparison of MEMS and OPA imaging laser radar
MEMS Phased array Core principle Micromechanical scanning Transmitter unit array + phase control Advantage Small size, light weight, large swing, small inertia Small size, light weight, no inertia, large swing (depending on unit spacing) Disadvantages Small target surface and low damage threshold Damage threshold is high, can form high power Craft difficulty Relatively easy Difficult (unit spacing is less than half of the optical wavelength) Cost Lower Reduce costs after batching Current research situation Relatively mature Immature -
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