ANALYSIS OF SYSTEMATIC ERRORS OF MOBILE LiDAR SYSTEMS: A SIMULATION APPROACH

Abstract

The systematic error analysis of the mobile LiDAR system (MLS) is always a challenging task in real-world situations. This challenge is mainly due to the mixture of systematic errors with non-systematic errors. To tackle this issue, in this paper, we introduce a conceptual model of an MLS simulator. The main advantage of the simulation-based approach is the full control over the erroneous systematic and non-systematic parameters that affect an MLS’s output. In the proposed simulation approach, we only take into account systematic errors that affect the simulated georeferenced point cloud. These systematic errors are as follows, POS-LiDAR boresight angles, POS-LiDAR leverarms, range offset, and scan angle offset. To simplify our analysis, we concentrate only on modeling the effects of systematic errors on planar targets and we focus solely on the terrestrial platform. Based on an independent analysis performed on each of the eight systematic errors of an MLS, to obtain strong visibility over systematic errors of an MLS, we suggest two planar targets of 1m x 1m dimensions with vertical and inclined orientations and a five-line pattern for MLS, two parallel and three side-looking passages. The proposed configuration generates an ideal input point cloud for the detection of systematic errors (except for the Z-Leverarm error) and ultimately it will lead to the proper input data for calibration of a terrestrial MLS. To validate our methodology, with an in-house assembled terrestrial MLS, we scanned a set of planar targets with three different orientations (vertical, inclined, and horizontal). This real-data validation test illustrated that with only two out of three planar targets (vertical and inclined) and with five out of six passages (two parallel to the planar targets and three side-looking passages), we will obtain expected visibility over the systematic errors of a terrestrial MLS, which approves the results with the simulation data.