Sebastian Skibinski’s research while affiliated with Fraunhofer Institute for Material Flow and Logistics and other places

In this paper we present a novel approach to the large-scale fusion of collaboratively acquired areal sensor data from a vehicle fleet, like temperatures, illuminations, frictions, traffic densities, signal strengths, air qualities, etc., which are intended for the incorporation into next-generation comfort functions and driver assistance systems. Our algorithm is based on the interpolation via RBFNs, but is extended, so that it fulfills the additional demands of the automotive industry. Therefore, we examine how to tackle large-scale, integrate timestamp-dependent weights, incorporate elliptic basis functions and realize an incremental computation. Finally, we investigate how our incremental approach to interpolation can be further improved by incorporating the Fast Gauss Transform, resulting in a reduction of the computation time by a factor of ten, if a batch insert or update (e. g. in the case of temporal reweighting) has to be applied. Our suggested approach to double-staged interpolation is applied exemplary to friction data, but can be also applied to other kinds of areal data as enumerated prior. Further, we present, that the first stage of the double-staged interpolation can be utilized for the determination of lane geometries, which can be consecutively used to annotate fused areal data with appropriate lane affiliations.

In this study, a novel approach for the detection of parcel loading positions on a pallet is presented. This approach was realized as a substantial change in comparison with traditional system design of contour detection in de-palletizing processes. Complex 3D-vision systems, costly laser scanners or throughput decreasing local sensor solutions integrated in grippers are substituted by a low-cost photonic mixing device (PMD) camera. By combining PMD technology and a predetermined model of loading situations, stored during assembling the pallet, this approach can compensate for the drawbacks of each respective system. An essential part of the approach are computer-graphics methods specific to the given problem to both detect the deviation between the nominal and the actual loading position and if necessary an automated correction of the packaging scheme. From an economic point of view, this approach can decrease the costs of mandatory contour checking in automated de-palletizing processes.

In this paper, we present a novel concept for the detection of loading positions of parcels or bins on a pallet to enable automated order picking using knowledge about the packing pattern model. The approach comprises (1) a new combination of pattern model data and PMD-camera-generated point clouds and (2) a novel concept of RFID data management using a Binary data on Tag / Schema on Net and semantic coding approach. The latter enables the use of additional services like storing of loading positions on auto-id devices (RFID-tags) in a wider concept of the Internet of Things, while the former presents an alternative approach in the context of contour check and position detection of unit loads for automated de-palletizing.