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Abstract
The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development of a new automatic calibration method that allows quick and frequent recovery of the inherent time-varying errors. The basic principle of the method is that the LiDAR’s internal systematic error parameters are estimated by constraining point clouds of some known and automatically detected cylindrical features such as lamp poles to fit to the 3D cylinder models. This is analogous to the plumb-line calibration method in which the lens distortion parameters are estimated by constraining the image points of straight lines to fit to the 2D line model. The calibration can be performed at every measurement epoch in both static and kinematic modes. Four real datasets were used to verify the method, two of which were captured in static mode and the other two in kinematic mode. The overall results indicate that up to approximately 72% and 41% accuracy improvement were realized as a result of the calibration for the static and kinematic datasets, respectively.
Highlights
Spinning multi-beam light detection and ranging (LiDAR) systems allow continuous acquisition of three-dimensional (3D) point clouds, which is important for many applications, such as unmanned vehicle navigation, mobile mapping and moving object tracking
The following advantages are specific to the kinematic mode calibration: (1) only data captured in one drive line are needed as no overlap of the feature point clouds is required and (2) global navigation satellite system (GNSS)/inertial measurement unit (IMU) measurements are not needed since the calibration is performed in the s-frame
The precisions vary within a 0.5 mm interval and are slightly lower in the higher and lower vertical angles as the corresponding range observations are longer for capturing vertical cylinders
Summary
Spinning multi-beam light detection and ranging (LiDAR) systems allow continuous acquisition of three-dimensional (3D) point clouds, which is important for many applications, such as unmanned vehicle navigation, mobile mapping and moving object tracking. Park et al [18] calibrated the exterior orientation parameters (EOPs), i.e., the rotational and translational parameters for an HDL-32E and interior orientation parameters of a RGB two-dimensional (2D) camera simultaneously using the corresponding vertices extracted from several polygonal planar targets (triangular and rhomboidal boards) These calibration methods require artificial targets, manually extracted features, specific scanning orientations or additional sensors such as video cameras, requirements that limit the flexibility for performing the calibration. The temporal stability of the range measurement of the Velodyne HDL-32E is first investigated as motivation for the development of a new automatic calibration method that can be performed at every measurement epoch without the need to setting up any artificial targets, using manually-extracted features or relying upon additional sensors. This is true for the calibration in kinematic mode as lamp poles can be more readily found around highway corridors compared to façades
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