@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} } @inproceedings{MuellerUrbanJutzi, author = {M{\"u}ller, Markus and Urban, S. and Jutzi, Boris}, title = {SQUEEZEPOSENET: Image based pose regression with small convolutional neural networks for real time uas navigation}, series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, volume = {IV-2/W3}, booktitle = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences}, doi = {10.5194/isprs-annals-IV-2-W3-49-2017}, pages = {49 -- 57}, abstract = {The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.}, language = {en} }