Robust and Accurate Image-Based Georeferencing Exploiting Relative Orientation Constraints

Abstract

ABSTRACT:Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2-3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

Portable Image-Based High Performance Mobile Mapping System in Underground Environments – System Configuration and Performance Evaluation

“…For the data post-processing workflow, we use pre-calibrated parameters of the panorama camera. Blaser et al (2018) describe the calibration procedure of the multi-head panorama camera Ladybug5. They estimate the interior orientation parameters (IOPs) of each camera head and the relative orientation parameters (ROPs) between the camera heads with a multi-constrained bundle adjustment.…”

Development of a Portable High Performance Mobile Mapping System Using the Robot Operating System

“…In application scenarios with high accuracy requirements, we aim to improve the LiDAR SLAM-based EOPs with a subsequent image-based georeferencing. For this purpose, we introduce the undistorted images into the incremental structure-from-motion (SfM) tool COLMAP (Schönberger & Frahm, 2016), which Cavegn et al (2018) extended with georeferencing capabilities by integrating prior EOPs and exploiting ROP constraints. Hence, we use LiDAR SLAM-based EOPs of the first camera (cam0) as initial values, and fix the ROPs of the other camera heads with the pre-calibrated values.…”

“…Hence, we use LiDAR SLAM-based EOPs of the first camera (cam0) as initial values, and fix the ROPs of the other camera heads with the pre-calibrated values. The complete process of integrated georeferencing based on COLMAP is described in detail by Cavegn et al (2018). Good lighting conditions and a structurally rich environment for proper and well-distributed feature detection are essential.…”

Accurate Visual Localization in Outdoor and Indoor Environments Exploiting 3d Image Spaces as Spatial Reference

“…We used data that we captured using our vehicle-based multisensor stereovision mobile mapping system, which was presented in several of our previous publications, including Cavegn et al (2018). It consists of three stereo systems featuring industrial cameras with CCD sensors and a GNSS/INS positioning system.…”

“…One part was acquired in November 2017 and is described in Cavegn et al (2018). The second part was captured in March 2018 and used in the work of Blaser et al (2018).…”