Laser Scanning

  • Localization and mapping using maximum consensus
    The long-term goal of this research topic is the creation of a localization and mapping algorithm, which is robust against outliers and disturbances. The research project is embedded in the Research Training Group “Integrity and Collaboration in Dynamic Sensor Networks (i.c.sens)” and primarily aims at improving integrity measures. The research is devided into two steps. In the first step, the localization considering the map as known is examined. In the second step, the problem will be extended treating the map as unknown as well.
    Team: Axmann, Brenner
    Year: 2020
  • TransMIT - Resource-optimised transformation of combined and separate drainage systems in existing quarters with high settlement pressure
    Increasing heavy rainfall events and growing urban districts pose great challenges for urban drainage. Using three neighbourhoods in the cities of Braunschweig, Hanover and Hildesheim as examples, it will be shown how urban development and water management aspects can be linked in the long term in neighbourhood planning.
    Leaders: Dr.-Ing. M. Beier; Prof. S. Köster, ISAH; Prof. Sester, ikg
    Team: Yu Feng, Udo Feuerhake
    Year: 2019
  • Object detection in airborne laser scanning (ALS) data using deep learning
    In partnership with the Lower Saxony State Office for Preservation of Historic Monuments, we are developing a method for automatically detecting archaeological objects in airborne laser scanning data. The type of objects to be detected are mainly those of interest by archaeologists such as heaps, shafts, charcoal piles, pits, barrows, bomb craters, hollow ways, etc. They could be point, linear, or areal objects. To this end, we are using deep learning techniques; namely, convolutional neural networks (CNNs) to classify height images from the region of interest. A combination of multiple (in most cases 5) CNN classifiers are then used to detect and localize objects of interest in a digital terrain model acquired from the region of interest.
    Team: Kazimi, Thiemann, Sester
    Year: 2018
    Sponsors: MWK Pro*Niedersachsen
  • 3D object extraction of high-resolution 3D point clouds
    National Survey Departments acquire area-wide, controlled airborne laser scanning (ALS) datasets with different point densities, which are at least classified into ground and non-ground points. The Working Committee of the Surveying Authorities of the Laender of the Federal Republic of Germany (AdV) is discussing about an update cycle of 10 years for ALS point clouds. The national survey departments also acquire 3D point clouds from aerial images every 2-3 years with high overlapping ratios using a method called Dense Image Matching (DIM). Those DIM point clouds have a high point density, which is equal to the original aerial image resolution. In addition, those DIM point clouds also contain radiometric information from the aerial images, but only reconstruct the surface due to image correlation. This project is split into four distinctive topics.
    Team: Politz, Sester
    Year: 2017
    Sponsors: Forschungs- und Entwicklungsvorhaben zwischen den Landesvermessungsämtern Niedersachsen, Schleswig-Holstein und Mecklenburg-Vorpommern
  • Collaborative acquisition of predictive maps
    Self-driving cars and robots that run autonomously over long periods of time need high precision and up-to-date models of the environment. Natural environments contain dynamic objects and change over time. Since a permanent observation of “everything” is impossible and there will always be a first time visit of the changed area, a map that takes into account the possibility of change is needed.
    Team: Schachtschneider, Brenner
    Year: 2017
    Sponsors: DFG-Graduiertenkolleg i.c.sens