Chinese Journal of Lasers, Volume. 49, Issue 19, 1910002(2022)

Review on Key Technologies of Lightweight Type-Aware LiDAR

Xiaolu Li*, Yier Zhou, Tengfei Bi, Ruiqin Yu, Zining Wang, Jianbin Huang, and Lijun Xu**
Author Affiliations
  • School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
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    Figures & Tables(14)
    Single photon detector. (a) 189 pixel×600 pixel SPAD array by Sony, Japan[8]; (b) 4 pixel×4 pixel SiPM detector by Israel Institute of Technology[10]
    Progress in lightweight type-aware LiDAR scanning technology. (a) MEMS LiDAR by Toyota Central R&D Labs, Japan[12]; (b) MEMS scanner array with 23 mm aperture by Fraunhofer Institute for Photonic Microsystems, Germany[15]; (c) OPA chip by Analog Photonics, USA[16]
    Fiber coupled 5 pixel×5 pixel flash LiDAR by Guilin University of Technology[20]
    Performance comparison of array LiDARs based on SoC
    Superiority of the EMD and VMD algorithms for processing nonlinear and non-stationary signal. (a) Denoising effects of EMD based on different criteria[62]; (b) denoising effects of EMD and VMD[67]
    Distortion and correction results of MEMS two-dimensional galvanometer scanning[87]. (a) Actual scanning image;(b) distorted image of grid test target; (c) grid image after distortion correction
    Angle errors of the front and back points comparing the effectiveness of the two-face, the length-consistency and the network methods[96]. (a) Horizontal angles; (b) vertical angles
    Calibration based on cylindrical target[102]. (a) The whole Velodyne point cloud; (b) layer and density segmentation; (c) 2D Hough circle detection (green); (d) radius histogram examination; (e) cylinder extraction (green dash) followed by cylinder fitting with blunder detection (magenta); (f) the segmented cylinders (blue)
    Schematic diagram of classification for lightweight type-aware LiDAR
    LiDAR systems in space exploration. (a) XSS-11 spaceborne scanning LiDAR system[110], Canada; (b) LiDAR system of Chang’e 3[118], China; (c) LiDAR system of Hayabusa2[119], Japan; (d) ALHAT flash LiDAR[120], USA; (e) OSIRIS scanning LiDAR[122], USA
    • Table 1. Characteristics of lightweight type-aware LiDAR scanning mechanism

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      Table 1. Characteristics of lightweight type-aware LiDAR scanning mechanism

      ScannerMechanicalMEMSFlashOPA
      PrincipleMechanical rotation, point by pointMicro-mirror vibrating, point by pointWhole scene with one single laserSolid-state beam steering, point by point
      AdvantagesHigh laser power, long detection rangeEase of integration, mass productionReal time, ease of integrationNo moving parts, ease of integration
      DrawbacksHeavy structureLimited scanning rangeHigh requirements on detector performanceProducing side lobes, leading to large energy loss
      Typical productsDJI-TELE 15 (China),Hesai-Pandar (China),Velodyne-VLP (USA),Huawei-96beam (China)Robosense-M1 (China),Luminar-Iris (USA),Innoviz-Pro (Israel)Ouster-DF (USA),Ibeo-NEXT (Germany),LuminWave-SMx (China)Quanergy-S3 (USA),Lumotive-X20 (USA),Litra-LT-X (China)
    • Table 2. Representative research results of filtering algorithms

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      Table 2. Representative research results of filtering algorithms

      Filtering methodCharacteristicsRepresentative algorithmsRef.
      Spatial domainEcho waveform convolved with filtering kernel; high efficiency and real-time filtering; affected by the filtering parametersGaussian filtering[51]
      Savitzky-Golay[54]
      Average filtering[55]
      Time-frequency domainRemoving high frequency noise in frequency domain; affected by threshold and basis functionFourier transform[56]
      Wavelet transform[57]
      EMDRemaining intrinsic mode functions (IMFs) in time domain; data driven; long execution timeEMD and derivative algorithms[6265]
      VMDRemaining IMFs in frequency domain; rigorous mathematical derivation; overcoming modal aliasing; long execution time, affected by the decomposition number K and the quadratic penalty αVMD and derivative algorithms[67-68]
      MCAMultiple echo waveforms stacked after shifting and aligning; noise suppression; hard to align signal; increase of heterogenous pointsMCA and derivative algorithms[69]
    • Table 3. LiDAR performance for docking in space

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      Table 3. LiDAR performance for docking in space

      YearCountryMissionPayloadPerformanceService time
      2005[110]CanadaExperimental Satellite System, XSS-11Ranging accuracy 5 cm@50 m2005—2007
      2014[111]USAArtemis ProgramOrion Multi-Purpose Crew VehiclePixel count 256 pixel×256 pixel, field of view 20°2014—now
      2011[113]ChinaChina Manned SpaceShenzhou-8Position accuracy <5 cm, attitude accuracy <0.3°2011.11.01—2011.11.17
      2012[114]USADragonPixel count 126 pixel×126 pixel, maximum range 750 m2012.10.08—2012.10.28
      2020[115]ChinaChina's Lunar Exploration ProjectChang’e 5Range 15 m—20 km2020.11.24—2020.12.17
    • Table 4. LiDAR performance for landing in space

      View table

      Table 4. LiDAR performance for landing in space

      Landing yearCountryMissionPayloadPerformanceService time
      2013[118]ChinaChina's Lunar Exploration ProjectChang’e 3Range 40-160 m, accuracy 15 cm2013—2016
      2018[119]JapanHayabusa2Hayabusa2Range 30 m-25 km, ranging resolution 0.5 m, ranging accuracy 1 m@30 m2014—2020
      2014[120]USAALHATALHAT probeRange 500-1000 m, ranging accuracy 8 cm
      Expected 2024[121]USAEuropa Lander missionEuropa probePixel count 2000 pixel×2000 pixel, ranging accuracy 5 cm@500 m
      2018[122]USAOSIRIS-RexOSIRIS-Rex spacecraftMaximum range >7.4 km, ranging accuracy <0.5 m2016—now
      2021[123]ChinaChina’s Mars Exploration missionTianwen 1Landing accuracy 3.1 km×0.2 km2020—now
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    Xiaolu Li, Yier Zhou, Tengfei Bi, Ruiqin Yu, Zining Wang, Jianbin Huang, Lijun Xu. Review on Key Technologies of Lightweight Type-Aware LiDAR[J]. Chinese Journal of Lasers, 2022, 49(19): 1910002

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    Paper Information

    Category: remote sensing and sensor

    Received: May. 7, 2022

    Accepted: Jul. 13, 2022

    Published Online: Sep. 20, 2022

    The Author Email: Li Xiaolu (xiaoluli@buaa.edu.cn), Xu Lijun (lijunxu@buaa.edu.cn)

    DOI:10.3788/CJL202249.1910002

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