Abstract
Drones may be more advantageous than fixed cameras for quality control applications in industrial facilities, since they can be redeployed dynamically and adjusted to production planning. The practical scenario that has motivated this paper, image acquisition with drones in a car manufacturing plant, requires drone positioning accuracy in the order of 5 cm. During repetitive manufacturing processes, it is assumed that quality control imaging drones will follow highly deterministic periodic paths, stop at predefined points to take images and send them to image recognition servers. Therefore, by relying on prior knowledge about production chain schedules, it is possible to optimize the positioning technologies for the drones to stay at all times within the boundaries of their flight plans, which will be composed of stopping points and the paths in between. This involves mitigating issues such as temporary blocking of line-of-sight between the drone and any existing radio beacons; sensor data noise; and the loss of visual references. We present a self-corrective solution for this purpose. It corrects visual odometer readings based on filtered and clustered Ultra-Wide Band (UWB) data, as an alternative to direct Kalman fusion. The approach combines the advantages of these technologies when at least one of them works properly at any measurement spot. It has three method components: independent Kalman filtering, data association by means of stream clustering and mutual correction of sensor readings based on the generation of cumulative correction vectors. The approach is inspired by the observation that UWB positioning works reasonably well at static spots whereas visual odometer measurements reflect straight displacements correctly but can underestimate their length. Our experimental results demonstrate the advantages of the approach in the application scenario over Kalman fusion.
Abstract (translated)
无人机在工业设施的质量控制应用中可能比固定相机更具优势，因为它们可以动态重新部署并根据生产计划进行调整。本文所描述的实际情景，即在汽车制造厂使用无人机进行图像采集，要求无人机定位精度达到5厘米。在重复生产过程中，假设质量控制成像无人机将遵循高度确定性的周期路径，在预定义的点停站拍照并将其发送到图像识别服务器。因此，通过依赖生产链时间表的先前知识，可以优化无人机的位置技术，使其始终在飞行计划的边界内，包括停站点和飞行路径之间。这包括减轻诸如无人机与现有无线信标之间视线阻塞的问题，传感器数据噪声以及视觉参考丢失等问题。我们提出了这种目的的自校正解决方案。它基于经过滤波和聚类的超宽带（UWB）数据进行视觉里程计读数的修正，作为直接Kalman融合的替代方案。该方法结合了这些技术的优点，只要至少有一个测量点上它们都能正常工作。它包括三个方法组件：独立Kalman滤波、通过流聚类进行数据关联以及根据累积校正向量基于传感器读数的相互校正。该方法源于观察到UWB定位在静态点上表现得相当好，而视觉里程计测量则准确反映直线位移，但可能低估其长度。我们的实验结果表明，在应用场景中，该方法相对于Kalman融合具有优势。
URL
https://arxiv.org/abs/2404.00426
