Comparative Analysis of Two New Approaches for LiDAR System Calibration

LiDAR systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the calibration of LiDAR systems has not followed the same trend. Current in-flight calibration methods have the following drawbacks: (i) They are time consuming and expensive; (ii) They are generally based on complicated and sequential calibration procedures; (iii) They require some effort for surveying the control surfaces; (iv) Some of the calibration methods involve manual and empirical procedures; (v) Some of the calibration methods require the availability of the LiDAR raw measurements such as ranges, mirror angles, as well as position and orientation information for each pulse; and (vi) There is no commonly accepted methodology since the calibration techniques are usually based on a manufacturer-provided software package and the expertise of the LiDAR data provider. As a result of the non-transparent and sometimes empirical calibration procedures, collected LiDAR data might exhibit systematic discrepancies between conjugate surface elements in overlapping strips. This paper presents two new calibration procedures for the estimation of biases in the system parameters that overcome the limitation of existing calibration procedures. The first presented method – denoted as the “Simplified Calibration Procedure” – makes use of the LiDAR point cloud from parallel LiDAR strips. The system biases are estimated using identified discrepancies between conjugate primitives in overlapping LiDAR strips. The second method – denoted as the “Quasi-Rigorous Calibration Procedure” – can deal with non-parallel strips, but requires time-tagged LiDAR point cloud and navigation data (trajectory position). Introduction: A LiDAR system is composed of a laser ranging and scanning unit and a position and orientation system (POS), which consists of an integrated differential global positioning system (DGPS) and an inertial measurement unit (IMU). The principle of laser ranging is to measure distances from the sensor to the ground. The GPS system provides position information and the IMU provides attitude information. The coordinates of the LiDAR points are the result of combining the derived measurements from each of its system components, as well as the mounting parameters relating such components. The relationship between the system measurements and parameters is embodied in the LiDAR equation (Vaughn et al., 1996). LiDAR system calibration, which aims at the estimation of the system parameters, is usually accomplished in several