The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B1
06 Jun 2016
 | 06 Jun 2016


A. P. Kerstinga and P. Friess

Keywords: MMS, lidar, accuracy, trajectory errors, system calibration, quality assurance

Abstract. Mobile Mapping Systems (MMS) consist of terrestrial-based moving platforms that integrate a set of imaging sensors (typically digital cameras and laser scanners) and a Position and Orientation System (POS), designed to collect data of the surrounding environment. MMS can be classified as “mapping-grade” or “survey-grade” depending on the system’s attainable accuracy. Mapping-grade MMS produce geospatial data suitable for GIS applications (e.g., asset management) while survey-grade systems should satisfy high-accuracy applications such as engineering/design projects. The delivered accuracy of an MMS is dependent on several factors such as the accuracy of the system measurements and calibration parameters. It is critical, especially for survey-grade systems, to implement a robust Quality Assurance (QA) procedure to ensure the achievement of the expected accuracy. In this paper, a new post-mission QA procedure is presented. The presented method consists of a fully-automated self-calibration process that allows for the estimation of corrections to the system calibration parameters (e.g., boresight angles and lever-arm offsets relating the lidar sensor(s) to the IMU body frame) as well as corrections to the system measurements (e.g., post-processed trajectory position and orientation, scan angles and ranges). As for the system measurements, the major challenge for MMS is related to the trajectory determination in the presence of multipath signals and GNSS outages caused by buildings, underpasses and high vegetation. In the proposed self-calibration method, trajectory position errors are properly modelled while utilizing an efficient/meaningful trajectory segmentation technique. The validity of the proposed method is demonstrated using a dataset collected under unfavorable GNSS conditions.