CN114266282A - 用于自动标记雷达数据的方法和系统 - Google Patents
用于自动标记雷达数据的方法和系统 Download PDFInfo
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Abstract
自动标记传感器数据的方法,车辆包括雷达探测手段、摄像机和激光雷达,各视场在重叠区域中至少部分重叠,提供一组雷达点、一组图像数据和一组激光雷达数据,自动为各雷达点指派可信度标记,方式是:校正图像数据,在摄像机和激光雷达的视场重叠区域中借助激光雷达数据校准基于摄像机的、由神经网络生成的深度估计,借此根据二维图像信息计算三维点云表现形式,用k近邻算法将三维点云表现形式与雷达点关联且将激光雷达数据与雷达点相关联以得出雷达/激光雷达可信度和雷达/摄像机可信度且将其合并为基于光学的可信度,借助用里程数据的跟踪为雷达点指派雷达/跟踪可信度,将其与基于光学的可信度组合且分配可信度标记。还提出实施该方法的系统。
Description
技术领域
本发明涉及一种用于在车辆中自动对从雷达探测手段获得的雷达数据或雷达点进行标记的方法,该车辆至少以辅助方式具有自动驾驶特性或者还可以为开发这种特性被用作数据采集器。此外,还提出一种实施该方法的系统。
背景技术
标记(Labeling)或标注是对数据或动态和静态类型的、表示为数据的对象和环境特征作的记号。在机器学习中、尤其是监控型学习中,标记是分析例如从雷达中获得的原始数据的必要步骤。可设想的雷达信号的标注在此为探测结果所对应的可信度,以便从探测结果区分出伪影。雷达伪影可被理解为诸如测量噪声、多次反射和错误探测之类的效应。而具有高可信度的点表示例如车辆周围环境、诸如墙壁之类的建筑结构以及柱子和其他在交通环境中的限制物的可靠雷达反射。探测层面上的雷达数据只能以非常复杂、耗时和昂贵的方式手动标记为点云数据(例如每个测量周期有几百个点,而每个测量周期可能处于两位数的毫秒范围内)。手动操作在此为误差或不一致性提供了很大的空间。此外,不存在可以实现该目的的自动标记过程。
在文献DE 10 2017 203 344 A1中描述了相对于本车(Host-Fahrzeug)对长形对象进行的检测,其中通过多个对象检测装置对这些对象进行检测并且在检测数据中识别出对应于这些长形对象的图案。检测数据在此由已由多个对象检测装置中的至少两个对象检测装置合并而成的数据组成。
欧洲文献EP 3 293 669 A1公开了一种具有摄像机的对象识别系统,该摄像机提供的输出具有有关可能出现的对象的信息。处理器基于至少一个另外的探测器(非摄像机)的信息,来选择摄像机输出中可能包含该可能出现的对象的部分并且确定该部分的对象附着性。
在文献WO 2019/161300 A1中示出了一种方法,其中将来自至少一个传感器的视场的传感器数据馈送给第一神经网络并且将所得对象数据指派给具有所探测的对象的相应位置。至少部分基于这些相应位置,将所探测对象处的集群特性馈送至第二神经网络并且由此获得置信值,其中,该置信值表示该集群与周围环境中的对象对应的可能性。
发明内容
在此背景下,本发明的目的在于,提出一种在探测层面运行的方法,用于自动对直接从雷达探测手段获得的雷达点进行标记。此外,应提供一种能实施该方法的系统。
为了实现上述目的,提出一种用于自动对所观察的场景的传感器数据进行标记的方法,其中,车辆包括雷达探测手段和作为光学传感器的至少一个摄像机以及激光雷达(LiDAR)并且所观察的场景例如是车辆周围环境,其中,雷达探测手段、摄像机和激光雷达分别具有该车辆的周围环境的至少一部分作为视场并且相应的视场至少部分地在相应的重叠区域中重叠。在一时间步长序列中,针对每个时间步长t,由雷达探测手段提供一组本身三维的雷达点、由至少一个摄像机提供一组本身二维的图像数据并且由激光雷达提供一组本身三维的激光雷达数据。针对每个时间步长,自动为各个雷达点指派相应的可信度标记,其方式为:
·通过图像校正和随后的透视变换对所述图像数据进行校正,来实现场景的直视图;
·在摄像机和激光雷达的视场的重叠区域中,借助于激光雷达数据来校准基于摄像机的、通过神经网络生成的深度估计;
·通过使用k近邻算法,不仅将三维点云表现形式与雷达点相关联还将激光雷达数据与雷达点相关联,由此在考虑欧几里得距离和不确定性的情况下,针对视场的重叠区域得出雷达/激光雷达可信度和雷达/摄像机可信度;
·将考虑相应视场情况下的雷达/激光雷达可信度和雷达/摄像机可信度合并为经组合的、基于光学的可信度;
·与之并行地,借助于在考虑该车辆的里程表数据的情况下进行的跟踪,为每个雷达点指派雷达/跟踪可信度;
·将基于光学的可信度和雷达/跟踪可信度组合并且随后分配二进制可信度标记,该二进制可信度标记表征相应的雷达探测手段是描述伪影还是描述真实可信的反射。
通过必然不会存在于二维图像中的深度估计可以进行到三维点云表现形式的换算。这种转换也对应于投影。
k近邻(kNN)算法围绕能够获取k个近邻的第一点在二维中张成圆并且在三维中张成球体,它们相应的半径增长到直至数量k个邻点位于该相应的半径之内。通过在逐像素遍历图像数据或三维点云表现形式以及激光雷达数据时进行相应k近邻搜索,可以将相应的点选择与雷达探测手段相关联。
按升序排列的雷达信号等级是:1.频谱中的原始数据。2.呈3D点的形式的探测数据。3.跟踪的聚集的对象清单。借助根据本发明的方法有利地尚在探测层面上时(即,紧接在获取/组合来自频谱中的原始数据的雷达点之后)并且在数据尚未被传递给上级应用之前进行关于雷达点的可信度的标记。因此,与三维雷达点一起将相应指派的可信度作为二进制可信度标记、例如值为0(伪影)或1(真实可信的反射)提供给上级应用。该上级应用例如可以是自主驾驶的功能或自动驾驶仪。因此,根据本发明的方法提供一种自动化的管线/流水线(Pipeline),用于自动计算来自相应光学传感器(在此来自具有雷达、激光雷达和至少一个摄像机的所谓的车载传感装置)的雷达信息的最明显或最可能的相关性。根据本发明的方法在英文中也可被称为“Radar Artifact Labeling Framework(雷达伪影标记框架)”,缩写为RALF。
在根据本发明的方法的一个实施方式中,为了对雷达点的雷达/跟踪可信度进行在时间方面的点追踪,使用了来自该车辆的里程表数据的偏航角的角速度和平移速度。这种雷达/跟踪可信度与经组合的、基于光学的可信度合并。里程表是另外的车载传感装置,其经由CAN总线提供其数据。
在根据本发明的方法的另一个实施方式中,雷达探测手段由多个雷达传感器构成。各个雷达传感器通常可以布置在车辆的各个拐角处和车辆的侧面上。同样,例如至少一个摄像机中的多个摄像机可以居中地布置在车辆的所有侧面上。
在根据本发明的方法的还又一个实施方式中,在激光雷达和摄像机的各个重叠区域中,针对相应视场的相应相同点检查相应可信度之间的一致性。这种图像数据和激光雷达数据中的信息之间的相互验证提高了可信度标记分配的可靠性。此外,由此有利地实现:至少一个摄像机的图像数据中的深度估计处于与根据所测量的激光雷达距离得出的同一标度下。
此外,要求保护一种用于自动对场景的传感器数据进行标记的系统,其中,车辆包括雷达探测手段和作为光学传感器的至少一个摄像机以及激光雷达。雷达探测手段、摄像机和激光雷达分别具有该车辆的周围环境的至少一部分作为视场,其中,相应的视场至少部分地在相应的重叠区域中重叠。在一时间步长序列中,针对每个时间步长t,由雷达探测手段能提供或准备一组本身三维的雷达点、由至少一个摄像机能提供或准备一组本身二维的图像数据并且由激光雷达能提供或准备一组本身三维的激光雷达数据。针对每个时间步长,为各个雷达点自动地指派或能够指派相应的可信度标记。该系统被配置成:
·通过图像校正和随后的透视变换对所述图像数据进行校正,来实现场景的直视图,
·在摄像机和激光雷达的视场的重叠区域中,借助于激光雷达数据来校准基于摄像机的、通过神经网络生成的深度估计,
·借助于基于摄像机的深度估计,根据二维图像信息来计算三维点云表现形式,
·通过使用k近邻算法,不仅将三维点云表现形式与雷达点相关联还将激光雷达数据与雷达点相关联,由此在考虑欧几里得距离和不确定性的情况下,针对视场的重叠区域得出雷达/激光雷达可信度和雷达/摄像机可信度,
·将雷达/激光雷达可信度和雷达/摄像机可信度合并为经组合的、基于光学的可信度,
·与之并行地,借助于在考虑该车辆的里程表数据的情况下进行的跟踪,来为每个雷达点指派雷达/跟踪可信度,
·将基于光学的可信度和雷达/跟踪可信度进行组合并且随后分配二进制可信度标记,该二进制可信度标记表征相应的雷达探测手段是描述伪影还是描述真实可信的反射。
在根据本发明的系统的一个设计方案中,该系统被配置成:为了对雷达点的雷达/跟踪可信度进行在时间方面的点追踪,使用来自该车辆的里程表数据的偏航角的角速度和平移速度,并且将雷达/跟踪可信度与经组合的、基于光学的可信度合并。
在根据本发明的系统的另一个设计方案中,雷达探测手段由围绕车辆分布的多个雷达传感器构成。
在根据本发明的系统的又一个设计方案中,该系统被配置成:在激光雷达和摄像机的各个重叠区域中,针对相应视场的相应相同点检查相应可信度之间的一致性并由此提高标记分配的可靠性。
本发明的其他优点和设计方案从说明书和附图得出。
不言而喻,在不脱离本发明范围的情况下,以上提到的特征以及仍将在以下说明的特征不仅能够在相应给出的组合中使用,而且还可以在其他组合中或者单独地使用。
附图说明
图1示意性地示出了根据本发明的方法的一个实施方式的结构架构。
图2示出了根据本发明的系统的一个设计方案的摄像机图像以及叠加有激光雷达数据的可信度标记的视图。
具体实施方式
图1示意性地示出了根据本发明的方法的一个实施方式的结构架构100。针对时间步长t,通过光学传感器(如一方面通过激光雷达111,另一方面通过观察车辆周围环境的摄像机112)来进行光学观察110。由摄像机112记录的图像数据相应地在校正模块113中进行校正。根据来自激光雷达111的激光雷达数据项Plidar,t101,将深度信息用于对图像数据进行深度估计114并且计算三维点云表现形式。在激光雷达匹配模块115中,通过使用k近邻算法将激光雷达数据项Plidar,t101与雷达数据项Pradar,t105相关联并生成雷达/激光雷达可信度wlm(pi,t)102。在摄像机匹配模块116中,通过使用k近邻算法将三维点云表现形式与雷达数据项Pradar,t105相关联并生成雷达/摄像机可信度wcm(pi,t)103。雷达/激光雷达可信度wlm(pi,t)102和雷达/摄像机可信度wcm(pi,t)103在组合模块117中合并成经组合的、基于光学的可信度wopt(pi,t)104,其中,也考虑激光雷达111和摄像机112的视场的非重叠区域,其中,非重叠区域表示相应其他光学传感器的盲角。此外,针对时间步长t,进行对雷达传感器121在时间方面的信号分析120,通过该信号分析在对雷达传感器121的原始数据进行汇集122之后得出雷达点Pradar,t105。同时根据车辆的里程表130以偏航角的角速度和平移速度vt存在里程表数据106。借助于里程表数据106,通过跟踪模块123执行对雷达/跟踪可信度wtr(pi,t)107的在时间方面的追踪。根据经组合的、基于光学的可信度wopt(pi,t)104与雷达/跟踪可信度wtr(pi,t)107的合并,获得用于在伪影与对象之间进行区分的二进制可信度标记(pi,t)来作为结果108。
图2中示出了根据本发明的系统的一个设计方案的摄像机图像210以及叠加有激光雷达数据201的可信度标记202、203的视图220。在此它们分别为相同的观察场景。
附图标记列表
100 方法的结构架构
101 Plidar,t(时间点t的激光雷达数据项)
102 wlm(pi,t)(雷达/激光雷达可信度)
103 wcm(pi,t)(雷达/摄像机可信度)
104 wopt(pi,t)(经组合的雷达/激光雷达可信度与雷达/摄像机可信度)
105 Pradar,t(时间点t的雷达数据项)
107 wtr(pi,t)(时间点t的点追踪可信度)
110 光学观察
111 激光雷达
112 周围环境摄像机
113 图像校正和透视变换
114 深度估计
115 激光雷达匹配模块
116 摄像机匹配模块
117 非重叠视场的组合
120 在时间方面的信号分析
121 雷达传感器
122 汇集
123 跟踪模块
130 里程表(行驶距离测量/位置/方位)
140 合并与标记
210 摄像机图像220叠加有激光雷达的可信度标记的视图
201 激光雷达
Claims (8)
1.一种用于自动对场景的传感器数据进行标记的方法(100),其中,车辆包括雷达探测手段(121)和作为光学传感器的至少一个摄像机(112)以及激光雷达(111),其中,雷达探测手段(121)、摄像机(112)和激光雷达(111)分别具有该车辆的周围环境的至少一部分作为视场,并且相应的视场至少部分地在相应的重叠区域中重叠,其中,在一时间步长序列中,针对每个时间步长t,由雷达探测手段(121)提供一组三维雷达点(105)、由所述至少一个摄像机提供一组二维图像数据(210)并且由所述激光雷达(111)提供一组三维激光雷达数据(101,201),并且其中,针对每个时间步长,自动地为各个雷达点指派相应的可信度标记(108,202,203),其方式为:
·通过图像校正(113)和随后的透视变换对所述图像数据(210)进行校正,来实现场景的直视图,
·在摄像机(112)和激光雷达(111)的视场的重叠区域中,借助于激光雷达数据(101,201)来校准基于摄像机的、通过神经网络生成的深度估计(114),
·借助于基于摄像机的深度估计(114),根据二维图像信息来计算三维点云表现形式,
·通过使用k近邻算法,不仅将三维点云表现形式与雷达点(105)相关联还将激光雷达数据(101,201)与雷达点(105)相关联,由此在考虑欧几里得距离和不确定性的情况下,针对视场的重叠区域得出雷达/激光雷达可信度(102)和雷达/摄像机可信度(103),
·将雷达/激光雷达可信度(102)和雷达/摄像机可信度(103)合并为经组合的、基于光学的可信度(104),
·与之并行地,借助于在考虑该车辆的里程表数据(106)的情况下进行的跟踪,来为每个雷达点指派雷达/跟踪可信度(107),
·将基于光学的可信度(104)和雷达/跟踪可信度(107)进行组合并且随后分配二进制可信度标记(108),该二进制可信度标记表征相应的雷达探测手段是描述伪影(202)还是描述可信的反射(203)。
2.根据权利要求1所述的方法,其中,为了对雷达点(105)的雷达/跟踪可信度(107)进行在时间方面的点追踪,使用了来自该车辆的里程表数据(106)的偏航角的角速度和平移速度,并且将雷达/跟踪可信度(107)与经组合的、基于光学的可信度(104)合并(140)。
3.根据前述任一权利要求所述的方法,其中,雷达探测手段(121)由多个雷达传感器构成。
4.根据前述任一权利要求所述的方法,其中,在激光雷达(111)和摄像机(112)的各个重叠区域中,针对相应视场的相应相同点检查相应可信度(102,103)之间的一致性并由此提高标记分配(140)的可靠性。
5.一种用于自动对场景的传感器数据进行标记的系统,其中,车辆包括雷达探测手段(121)和作为光学传感器的至少一个摄像机(112)以及激光雷达(111),其中,雷达探测手段(121)、摄像机(112)和激光雷达(111)分别具有该车辆的周围环境的至少一部分作为视场,并且相应的视场至少部分地在相应的重叠区域中重叠,其中,在一时间步长序列中,针对每个时间步长t,由雷达探测手段(121)能提供或准备一组三维雷达点(105)、由至少一个摄像机能提供或准备一组二维图像数据(210)并且由激光雷达(111)能提供或准备一组三维激光雷达数据(101,201),并且其中,针对每个时间步长,为各个雷达点自动地指派或能指派相应的可信度标记(108,202,203),其中,该系统被配置成:
·通过图像校正(113)和随后的透视变换对所述图像数据(210)进行校正,来实现场景的直视图,
·在摄像机(112)和激光雷达(111)的视场的重叠区域中,借助于激光雷达数据(101,201)来校准基于摄像机的、通过神经网络生成的深度估计(114),
·借助于基于摄像机的深度估计(114),根据二维图像信息来计算三维点云表现形式,
·通过使用k近邻算法将三维点云表现形式与雷达点(105)相关联并且将激光雷达数据(101,201)与雷达点(105)相关联,由此在考虑欧几里得距离和不确定性的情况下,针对视场的重叠区域得出雷达/激光雷达可信度(102)和雷达/摄像机可信度(103),
·将雷达/激光雷达可信度(102)和雷达/摄像机可信度(103)合并为经组合的、基于光学的可信度(104),
·与之并行地,借助于在考虑该车辆的里程表数据(106)的情况下进行的跟踪,来为每个雷达点指派雷达/跟踪可信度(107),
·将基于光学的可信度(104)和雷达/跟踪可信度(107)进行组合并且随后分配二进制可信度标记(108),该二进制可信度标记表征相应的雷达探测手段是描述伪影(202)还是描述真实可信的反射(203)。
6.根据权利要求5所述的系统,该系统被配置成:为了对雷达点(105)的雷达/跟踪可信度(107)进行在时间方面的点追踪,使用来自该车辆的里程表数据(106)的偏航角的角速度和平移速度,并且将雷达/跟踪可信度(107)与经组合的、基于光学的可信度(104)合并(140)。
7.根据权利要求5或6所述的系统,其中,雷达探测手段(121)由围绕车辆分布的多个雷达传感器构成。
8.根据权利要求5至7中任一项所述的系统,该系统被配置成:在激光雷达(111)和摄像机(112)的各个重叠区域中,针对相应视场的相应相同点检查相应可信度(102,103)之间的一致性并由此提高标记分配(140)的可靠性。
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