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A Rigorous Observation Model for the Risley Prism-Based Livox Mid-40 Lidar Sensor



Filetype [PDF - 10.33 MB]

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  • Journal Title:
    Sensors
  • Personal Author:
    Brazeal, Ryan G. ; Wilkinson, Benjamin E. ; Hochmair, Hartwig H. ORCID ICON IMG
  • NOAA Program & Office:
    NOS (National Ocean Service)
  • Description:
    Modern lidar sensors are continuing to decrease in size, weight, and cost, but the demand for fast, abundant, and high-accuracy lidar observations is only increasing. The Livox Mid-40 lidar sensor was designed for use within sense-and-avoid navigation systems for autonomous vehicles, but has also found adoption within aerial mapping systems. In order to characterize the overall quality of the point clouds from the Mid-40 sensor and enable sensor calibration, a rigorous model of the sensor’s raw observations is needed. This paper presents the development of an angular observation model for the Mid-40 sensor, and its application within an extended Kalman filter that uses the sensor’s data to estimate the model’s operating parameters, systematic errors, and the instantaneous prism rotation angles for the Risley prism optical steering mechanism. The analysis suggests that the Mid-40’s angular observations are more accurate than the specifications provided by the manufacturer. Additionally, it is shown that the prism rotation angles can be used within a planar constrained least-squares adjustment to theoretically improve the accuracy of the angular observations of the Mid-40 sensor.
  • Keywords:
  • Source:
    Sensors, 21(14)
  • DOI:
    https://doi.org/10.3390/s21144722
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA18NOS4000198
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
  • Download URL:
    https://repository.library.noaa.gov/view/noaa/32976/noaa_32976_DS1.pdf
  • File Type:
    Filetype[PDF - 10.33 MB]
  • Collection(s):
    National Ocean Service (NOS)
  • Main Document Checksum:
    urn:sha-512:c2884eadce04c8f716e852fe3d3a091e3369f1a97948853ff06625ee17881f58595d37244db68c736c506dd0b54b886501df1e85501d2984e776dc49112432f9
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