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适用汽车智能驾驶的多光谱激光雷达波长选择可行性研究 被引量:4

The feasibility study of wavelength selection of multi-spectral LIDAR for autonomous driving
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摘要 在汽车智能驾驶系统中,激光雷达由于其独特的三维成像能力,成为场景探测感知传感器群组中不可或缺的组成部分。为提升单一波长激光雷达在物性探测分类和状态上的性能,借鉴多光谱探测具有物性探测能力的原理,论文对适用于汽车智能驾驶的多光谱激光雷达的波段选择进行了可行性研究,利用主成分分析法对智能驾驶中典型目标进行光谱计算及分析,结合激光光源特性以及光电探测器的特性,综合多光谱激光雷达波段选择方法和智能驾驶应用场景中典型目标地物光谱特性,以及商用激光雷达的可获得性,得出了适用汽车智能驾驶的多光谱激光雷达的波长可以选择808 nm、905 nm、1 064 nm、1 310 nm,并通过测试验证了多光谱激光雷达所选波长的有效性。 In the autonomous driving system of automobile,in order to improve the performance of single-wavelength LIDAR in physical property detection classification and state,and draw lessons from the principle that multi-spectral detection has physical property detection ability,this paper studies the band selection of multi-spectral LIDAR,calculates and analyses the spectrum of typical targets in autonomous driving by using principal component analysis method.The characteristics of laser source and detector,the band selection method of multi-spectral LIDAR,the spectral characteristic analysis of typical targets for autonomous driving application scenarios and the availability of commercial LIDAR are synthesized.The central wavelength of the multi-spectral LIDAR suitable for autonomous driving of automobiles is 808 nm,905 nm,1064 nm and 1310 nm.The validity of the selected wavelength of the multi-spectral LIDAR is verified by testing.
作者 宋绍京 陈育伟 胡海江 胡金艳 龚玉梅 邵慧 SONG Shao-Jing;CHEN Yu-Wei;HU Hai-Jiang;HU Jin-Yan;GONG Yu-Mei;SHAO Hui(Department of Communication and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China;Department of Remote Sensing and Photogrammetry,Finnish Geospatial Research Institute,National Land Survey of Finland,02431 Masala,Finland;School of Electronic and Information Engineering,Anhui Jianzhu University,Hefei 230601,China)
出处 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2020年第1期86-91,共6页 Journal of Infrared and Millimeter Waves
基金 上海第二工业大学校重点学科建设项目(XXKZD1605) 校企合作汽车电子联合实验中心建设项目资助(A11NH190704)~~
关键词 场景感知 主成分分析 智能驾驶 激光雷达 波长选择 band selection principal component analysis autonomous driving LIDAR
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