Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Efficient Tour Planning for a Measurement Vehicle by Combining Next Best View and Traveling Salesman
    ( 2021)
    Gehrung, Joachim
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    Path planning for a measuring vehicle requires solving two popular problems from computer science, namely the search for the optimal tour and the search for the optimal viewpoint. Combining both problems results in a new variation of the Traveling Salesman Problem, which we refer to as the Explorational Traveling Salesman Problem. The solution to this problem is the optimal tour with a minimum of observations. In this paper, we formulate the basic problem, discuss it in context of the existing literature and present an iterative solution algorithm. We demonstrate how the method can be applied directly to LiDAR data using an occupancy grid. The ability of our algorithm to generate suitably efficient tours is verified based on two synthetic benchmark datasets, utilizing a ground truth determined by an exhaustive search.
  • Publication
    Extrinsic self-calibration of an operational mobile LiDAR system
    ( 2020)
    Diehm, Axel
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    Gehrung, Joachim
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    In this paper, we describe a method for automatic extrinsic self-calibration of an operational mobile LiDAR sensing system (MLS), that is additionally equipped with a POS position and orientation subsystem (e.g., GNSS/IMU, odometry). While commercial mobile mapping systems or civil LiDAR-equipped cars can be calibrated on a regular basis using a dedicated calibration setup, we aim at a method for automatic in-field (re-)calibration of such sensor systems, which is even suitable for future military combat vehicles. Part of the intended use of a mobile LiDAR or laser scanning system is 3D mapping of the terrain by POS-based direct georeferencing of the range measurements, resulting in 3D point clouds of the terrain. The basic concept of our calibration approach is to minimize the average scatter of the 3D points, assuming a certain occurrence of smooth surfaces in the scene which are scanned multiple times. The point scatter is measured by local principal component analysis (PCA). Parameters describing the sensor installation are adjusted to reach a minimal value of the PCA's average smallest eigenvalue. While sensor displacements (lever arms) are still difficult to correct in this way, our approach succeeds in eliminating misalignments of the 3D sensors (boresight alignment). The focus of this paper is on quantifying the influence of driving maneuvers and, particularly, scene characteristics on the calibration method and its results. One finding is that a curvy driving style in an urban environment provides the best conditions for the calibration of the MLS system, but other structured environments may still be acceptable.
  • Publication
    A representation of MLS data as a basis for terrain navigability analysis and sensor depolyment planning
    ( 2019)
    Gehrung, Joachim
    ;
    Hebel, Marcus orcid-logo
    ;
    Arens, Michael
    ;
    Stilla, Uwe
    Recording an ever-changing urban environment in a structured manner requires sensor deployment planning. In case of mobile sensor platforms, this also includes verifying the terrain navigability. Solving both tasks would usually require different application-specific data structures and tools. In this work, we propose a theoretical framework that provides a uniform representation for spatial information as well as the tools required to combine, manipulate and visualize it. We provide an efficient implementation of the framework utilizing octree-based evidence grids. Our approach can be used to solve complex tasks by combining simple spatial information sources, which we demonstrate by providing simple solutions to the aforementioned applications. Despite the use of a volumetric approach, our runtimes are within the range of minutes.