通过协作补全传感器盲区,让微型飞行灯点精准定位三维形状。
Swazure: Swarm Measurement of Pose for Flying Light Specks

- 利用飞灯点间协作推断彼此位姿,解决传感器范围外的定位难题。
- 中等尺寸飞灯点可实现100%邻居位姿测量,大尺寸可能因遮挡失效。
- 提出两种避障策略,其中'移动遮挡物'可解决约30%最坏情况遮挡。
利用配备光源的微型无人机构建三维多媒体显示(飞灯点FLS)。飞灯点集群需根据点云坐标精确定位,以呈现复杂3D形状与动画。这依赖于无人机间通过摄像头等传感器测量相对位姿,而传感器仅在特定有效范围内精度最高。当某飞灯点超出另一飞灯点传感器的有效范围时,如何获取其位姿成为挑战。本文提出新型协同定位技术Swazure,通过飞灯点间的合作填补缺失数据。该方法通过抽象传感器物理特性,使点云数据与硬件无关。飞灯点尺寸相对于点云最小点距是关键参数:中等尺寸下,Swazure可定位100%邻居;较大尺寸可能导致遮挡,影响相对位姿估计。为此提出两种启发式策略——'移动遮挡物'和'移动源点'。实验表明,'移动遮挡物'在最坏情况下可解决约30%的遮挡问题。
原文摘要 · Abstract (English)
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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