@inproceedings{UngenLouwPalm2021,
  author    = {Ungen, Marc and Louw, Louis and Palm, Daniel},
  title     = {Multi-sensor identification of unmarked piece goods},
  booktitle = {Proceedings of the 2nd Conference on Production Systems and Logistics : CPSL 2021, 25-28 May 2021, Vancouver, Canada},
  issn      = {2701-6277},
  doi       = {10.15488/11236},
  institution = {ESB Business School},
  pages     = {740 -- 748},
  year      = {2021},
  abstract  = {The seamless fusion of the virtual world of information with the real physical world of things is considered the key for mastering the increasing complexity of production networks in the context of Industry 4.0. This fusion, widely referred to as the Internet of Things (IoT), is primarily enabled through the use of automatic identification (Auto-ID) technologies as an interface between the two worlds. Existing Auto-ID technologies almost exclusively rely on artificial features or identifiers that are attached to an object for the sole purpose of identification. In fact, using artificial features for the purpose of identification causes additional efforts and is not even always applicable. This paper, therefore, follows an approach of using multiple natural object features defined by the technical product information from computer-aided design (CAD) models for direct identification. By extending optical instance-level 3D-Object recognition by means of additional non-optical sensors, a multi-sensor automatic identification system (AIS) is realised, capable of identifying unpackaged piece goods without the need for artificial identifiers. While the implementation of a prototype confirms the feasibility of the approach, first experiments show improved accuracy and distinctiveness in identification compared to optical instance-level 3D-Object recognition. This paper aims to introduce the concept of multisensor identification and to present the prototype multi-sensor AIS.},
  language  = {en}
}