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Improving GPS trajectories using 3D city models and kinematic point clouds

 
: Dehbi, Y.; Lucks, Lukas; Behmann, J.; Klingbeil, L.; Plümer, L.

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Fulltext ()

Ellul, C. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
4th International Conference on Smart Data and Smart Cities 2019 : 1-3 October 2019, Kuala Lumpur, Malaysia
Istanbul: ISPRS, 2019 (ISPRS Annals IV-4/W9)
pp.35-42
International Conference on Smart Data and Smart Cities <4, 2019, Kuala Lumpur>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
mobile mapping system; city model; point cloud

Abstract
Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.

: http://publica.fraunhofer.de/documents/N-574623.html