Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera. To analyze this camera performance, an experimental study in a thermal chamber has been carried out. From this experiment, it has been seen that produced errors can be modeled as an hyperbolic paraboloid function. To compensate for this error, a two-step method that first computes the error and then corrects it has been proposed. To compute the error two possible strategies are proposed, one based on the infrared distortion map and the other on the depth map. The proposed method has been tested in an experimental scenario with different Orbbec Astra cameras and also in a real environment. In both cases, its good performance has been demonstrated. In addition, the method has been compared with the Kinect v1 achieving similar results. Therefore, the proposed method corrects the error due to temperature, is simple, requires a low computational cost and might be applicable to other similar cameras.
The animal feed supply chain to farm, mainly represented by the feed suppliers and livestock farmers, currently faces great inefficiencies due to outdated supply chain management. Stakeholders struggle with the timing and quantity evaluation when restocking their feed bins, significantly affecting cost and labour efficiency. However, the lack of accurate and cost-effective sensors to measure stock levels of solid materials stored in containers and open piles is preventing the implementation of these strategies in a large number of industrial sectors. In these cases, traditional technologies cannot offer a convenient solution due to an inevitable trade-off between accuracy and cost. This work develops an integral feedstock management system to optimise the entire supply chain. A new monitoring system based on an RGB-D sensor is presented as well as the data processing pipeline from raw depth measurements to bin specific daily consumption rates. K EYWORDSInventory management, Vendor Managed Inventories, Internet of Things.
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