Abstract
Abstract. Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access. Autonomous monitoring and navigation requires a background knowledge on the surroundings of the vehicle. Most mission planing systems assume collision-free pre-defined paths and do not tolerate a GPS signal outage. Our approach makes weaker assumptions. This paper introduces a mission planing platform allowing for the integration of environmental prior knowledge such as 3D building and terrain models. This prior knowledge is integrated to pre-compute an octomap for collision detection. The semantically rich building models are used to specify semantic user queries such as roof or facade inspection. A reasoning process paves the way for semantic mission planing of hidden and a-priori unknown objects. Subsequent scene interpretation is performed by an incremental parsing process.
Highlights
Unmanned aerial vehicles are widely used to survey and act in several complex environments and support solving various tasks
Semantically rich building models in the sense of CityGML (Groger et al, 2012) or CityJSON (Ledoux et al, 2019) are relevant since they allow to represent the meaning of geometric objects such as roofs or balconies
We introduce in this paper a mission planing platform allowing for the integration of environmental prior knowledge, in particular 3D building and terrain models
Summary
Unmanned aerial vehicles are widely used to survey and act in several complex environments and support solving various tasks. Semantically rich building models in the sense of CityGML (Groger et al, 2012) or CityJSON (Ledoux et al, 2019) are relevant since they allow to represent the meaning of geometric objects such as roofs or balconies To this aim, we introduce in this paper a mission planing platform allowing for the integration of environmental prior knowledge, in particular 3D building and terrain models. The mission planing platform has been used as starting step to predict in particular previously unobserved substructures based on symmetry recognition (Loch-Dehbi et al, 2013) as well as in the incremental semantic interpretation of a geometric map consisting mainly of LoD2 building models The latter is available in different levels of detail and is based on the flight movement of the UAV using attributed grammars and relational learning (Dehbi et al, 2016).
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