Smart And Flexible Mobile Data Collector For GIS
|Host Organisation:||Geonoesis Ltd|
|Partner Organisation(s):||PA 1: Cyprus University of Technology PA 2: Ministry of Transport, Communication and Works|
|Project Budget:||246.960,00 €|
|IDEK Funding:||198.396,00 €|
Much of the initial investment in modern GIS is spent on initial data collection. Traditional data collection performed with handheld GPS by foot, Ɵons and Works increases the respective cost. In order to minimize the resources employed for the data collection process, major surveying instrument manufacturers have presented several mobile mapping systems, having many advantages including this minimization of the resources in the field, the review the data collection process and effectively document the GIS data. On the other hand, their cost is up to hundreds of thousand Euros, whereas the usage and exploitation of the produced huge data sets make their use difficult and often counterproductive. Recently some lower cost and lower accuracy systems have been proposed, however, their cost remains in the scale of several tens of thousands or Euros while the accuracy they achieve remains questionable.
MOBILO aims to overcome these systems drawbacks. Specifically, we propose a low-cost mobile mapping system which consists of a GPS / GNSS RTK, an inertial INS / IMU system gathering position and orientation data, as well as video cameras to collect image data. We propose the development of two low cost alternative solutions (a) one with low-cost cameras (e.g., action cams) together with any existing RTK GPS, an alternative which reduces the cost of employed hardware to several hundreds of Euros, and targets to a specific customer group i.e., professional surveyors, and (b) high-end machine vision mission cameras together with RTK GPS / GNSS, INS / IMU which targets to more advanced users. We have already employed the basic ideas of the proposal in a rather simple form, in order to record city infrastructure objects and road equipment. Our final goal is to lower the typical errors achieved by the proposed system in its present form. The results so far establish that the proposed system minimizes the data collection time, while providing the tools for high productivity in the office, thus reducing the costs of mapping large areas.