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Simulation of the scanned point cloud pattern aiding lidar data acquisition planning process for mobile mapping system
Date Issued
January 1, 2020
Type
Conference Paper
Abstract
Point cloud data obtained from LIDAR technology is widely used in many sciences and engineering disciplines. Common use-cases of point cloud data are surface models construction or 3D modeling of the interest objects which can then be exploited in a variety of applications that cannot be exhaustively listed, providing solutions to answer specific questions or problems. Many factors in data collection and processing steps dictate the quality of the point cloud which has a direct impact on the quality of the to-be-created object models. Point density is one of the most important properties of the point cloud dataset that influences how the feature extraction process can be efficiently performed to extract points for object model reconstruction. Therefore, data acquisition planning is required to ensure the sufficiency of the point density of the collected dataset. In this work, a prototype of the point cloud simulation platform is developed for aiding data acquisition planning tasks by simulating the expected scanned results from the terrestrial mobile mapping system (MMS). With this platform, MMS devices in the off-the-shelf market can be selected by users to perform the simulation, and the platform will automatically retrieve their associated specifications of the selected MMS. On the other hand, MMS specifications customized by users are also allowed to be adopted. Additionally, the operation parameters such as driving speed of data collection process, the height of the vehicle on which the MMS is mounted, and the nominal distances between the scanner unit and the selected target can be specified. Based on those input parameters the platform simulates the scanned pattern on the scanned scene which is set to be plane in both horizontal and vertical directions which represent ground surface and wall, respectively. With the simulated scanned pattern, the user can select to overlay signalized/reflective targets on the scanned scenes to visual the scan pattern. The platform offers selectable standard designs of reflective targets, such as a cross sign, circle, and chess, the size of them can be altered based on user requirements. With this point cloud simulation platform, users can foresee an expected scanned pattern on their objects and subsequently lead to the capability of estimating point cloud density. Furthermore, users can perform tuning of parameters related to operation scenarios and re-simulate the scan results which can greatly benefit the data acquisition planning process since it can be done in house with less time consuming and in a cost-efficient manner.
Citation
Acrs 2020 41st Asian Conference on Remote Sensing, 2020