Documenting machinery movements by using positioning technologies, such as global navigation satellite systems (GNSS), is essential to understand and further improve construction processes. However, before measurements can be meaningfully analysed the documented movements should be filtered to exclude outliers. Eliminating outliers manually is a time-demanding process, while automatic filtering can be inaccurate. In particular, path elements may get lost if machine-specific movements are misconceived as noisy data. As a trade-off, we propose an information fusion approach to filter paths of construction machines in a semi-automatic way. The approach allows an expert to relate “hard” sensor and “soft” field records with his or her expectations about how machines can move in real construction projects. Specially developed open-source software illustrates the proposed approach for filtering the documented paths of machines involved in road paving projects. The initial testing of the developed software showed its suitability to filter outliers in GNSS data and identified possibilities for further improvements.
|Title of host publication||eg-ice 2013 20th international workshop: intelligent computing in engineering, July 1 - 3, 2013, Vienna, Austria|
|Editors||G. Suter, P. de Wilde, Y. Rafiq|
|Place of Publication||Vienna, Austria|
|Number of pages||10|
|Publication status||Published - 1 Jul 2013|
|Event||20th International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2013 - Vienna, Austria|
Duration: 1 Jul 2013 → 3 Jul 2013
Conference number: 20
|Workshop||20th International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2013|
|Period||1/07/13 → 3/07/13|
Vasenev, A., Ionita, D., Bijleveld, F., Hartmann, T., & Doree, A. G. (2013). Information fusion of GNSS sensor readings, field notes, and expert's a priori knowledge. In G. Suter, P. de Wilde, & Y. Rafiq (Eds.), eg-ice 2013 20th international workshop: intelligent computing in engineering, July 1 - 3, 2013, Vienna, Austria (pp. 1-10). Vienna, Austria: TU Wien.