|Student:||Syed Zill-E- Hussnain|
|Timeline:||December 2014 - 30 November 2018|
This project aims to improve the position estimation of mobile mapping platforms. Mobile Mapping (MM) is a technique to obtain geo-information on a large scale using sensors mounted on a car or another vehicle. Under normal conditions, accurate positioning is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS).
However, especially in urban areas, where building structures impede a direct line-of-sight to navigation satellites or lead to multipath effects, MM-derived products, such as laser point clouds or images, lack the expected reliability and contain an unknown positioning error.
This project is addressing that problem by employing high-resolution aerial nadir and oblique imagery as reference data to be independent of GNSS measurements in urban areas. To achieve maximum flexibility towards different MM systems, the project is split into two parts. Since not all MM systems employ laser scanners and cameras, and these systems and data demand different approaches, Zille Hussnain is focusing on the utilisation of LiDAR data, whereas Phillipp Jende concentrates on MM images.
This part of the research project is treating the correction of MM platforms carrying Mobile Laser Scanning (MLS) systems. By establishing precise correspondences between aerial images and MLS data, reliable ground control for an estimation technique can be provided.
During the process of mobile mapping, an MLS system continuously acquires laser range measurements resulting in a 3D point cloud.
High-level features, such as kerbstones, zebra crossings and street lights can be extracted from the point cloud by utilising knowledge-based methods. To gain more potential feature matches between the data sets, corners and edges can be detected in the MLS data set as well. Based on 3D point cloud information, a confined search for correspondences in the aerial data sets will be conducted.
In conjunction with INS readings, identified correspondences introducing ground control serve as an input for a filtering technique (i.e. Kalman or Particle filtering) allowing for a re-estimation of the vehicle’s position over time resulting in a corrected trajectory of the MM platform (see Figure 1)
Figure 1 Correction of MLS platform's trajectory based on high and low-level features.