@article{MuellerHillemannWeinmannetal., author = {M{\"u}ller, Markus and Hillemann, Markus and Weinmann, Martin and Jutzi, Boris}, title = {Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features}, series = {Remote sensing}, volume = {11}, journal = {Remote sensing}, number = {16}, doi = {10.3390/rs11161955}, abstract = {Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the R{\´e}nyi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.}, language = {en} } @inproceedings{WeinmannMuellerHillemannetal., author = {Weinmann, Martin and M{\"u}ller, Markus and Hillemann, Markus and Reydel, N. and Hinz, S. and Jutzi, Boris}, title = {Point cloud analysis for UAV-borne laser scanning with horizontally and vertically oriented line scanners - Concept and first results}, series = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {XLII-2/W6}, booktitle = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {10.5194/isprs-archives-XLII-2-W6-399-2017}, pages = {399 -- 406}, abstract = {In this paper, we focus on UAV-borne laser scanning with the objective of densely sampling object surfaces in the local surrounding of the UAV. In this regard, using a line scanner which scans along the vertical direction and perpendicular to the flight direction results in a point cloud with low point density if the UAV moves fast. Using a line scanner which scans along the horizontal direction only delivers data corresponding to the altitude of the UAV and thus a low scene coverage. For these reasons, we present a concept and a system for UAV-borne laser scanning using multiple line scanners. Our system consists of a quadcopter equipped with horizontally and vertically oriented line scanners. We demonstrate the capabilities of our system by presenting first results obtained for a flight within an outdoor scene. Thereby, we use a downsampling of the original point cloud and different neighborhood types to extract fundamental geometric features which in turn can be used for scene interpretation with respect to linear, planar or volumetric structures.}, language = {en} }