Swazure: 飞行光点的群体姿态测量
Swazure: Swarm Measurement of Pose for Flying Light Specks
AI总结：
针对飞行光点（FLS）在传感器最佳范围外无法相互跟踪的问题，提出Swazure方法，通过FLS间协作解决缺失传感器数据，实现物理数据独立性，并引入两种启发式算法处理遮挡。
摘要：
可以使用配置有光源的微型无人机（称为飞行光点，FLS）构建3D多媒体显示。FLS群体通过定位来照亮复杂的3D形状和动画序列，这些序列与点云中的点坐标一致。这要求FLS使用相机等传感器精确测量彼此之间的相对姿态。这类传感器有一个最佳范围，在此范围内它们提供最高的精度。一个挑战是，根据点云数据，一个FLS如何跟踪其传感器最佳范围之外的另一个FLS。我们通过提出一种名为Swazure的新技术来解决这一挑战，该技术利用FLS之间的协作来弥补缺失的传感器数据。它通过抽象传感器的物理特性来实现物理数据独立性，使点云数据独立于传感器硬件。FLS的尺寸相对于点云中两点之间的最小距离是一个重要参数。对于中等大小的FLS，Swazure能够定位100%的FLS邻居。较大的FLS尺寸可能导致潜在的遮挡，从而阻止Swazure量化相对姿态。我们提出了两种启发式算法，即移动遮挡物和移动源，以解决这一限制。我们的实验结果表明，移动遮挡物启发式算法更优，在最坏情况下能解决约30%的遮挡问题。
英文摘要：
One may construct a 3D multimedia display using miniature drones configured with light sources, Flying Light Specks (FLSs). Swarms of FLSs localize to illuminate complex 3D shapes and animated sequences consistent with the coordinates of points in a point cloud. This requires FLSs to accurately measure their pose relative to one another using sensors such as cameras. Such sensors have a sweet range in which they provide the highest accuracy. A challenge is how an FLS tracks another FLS outside its sensor's sweet range, dictated by the point cloud data. We address this challenge by proposing a novel technique called Swazure that solves the missing sensor data using cooperation among FLSs. It implements physical data independence by abstracting the physical characteristics of the sensors, making point cloud data independent of the sensor hardware. The size of an FLS relative to the minimum distance between points of a point cloud is an important parameter. With medium-sized FLSs, Swazure is able to position 100% of the FLS's neighbors. Larger FLS sizes may result in potential obstructions that prevent Swazure from quantifying relative pose. We present two heuristics, Move Obstructing and Move Source, to address this limitation. Our experimental results show the superiority of the Move Obstructing heuristic which resolves approximately 30% of obstructions in the worst case scenario.
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