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Object recognition using low light level 3D point clouds

机译:使用弱光3D点云的物体识别

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Target recognition is a key aspect for many applications. Rapidly maturing small sensor platforms continually require better, more agile sensor performance coupled with smaller, lighter, and faster sensor implementations. Additionally, longer range applications necessitate more efficient use of photons received from active illumination. We describe a potential approach to overcoming both issues based on photon counting laser radar, which performs pattern recognition using images with very few detected photo-events. Previous work using intensity images show near ideal pattern recognition with as low as 50 photo-detections. We investigate through simulation an extension of prior work to 3D point cloud imagery.
机译:目标识别是许多应用程序的关键方面。快速成熟的小型传感器平台不断需要更好,更敏捷的传感器性能,以及更小,更轻,更快的传感器实现。另外,更长距离的应用需要更有效地利用从主动照明中接收到的光子。我们描述了一种基于光子计数激光雷达来克服这两个问题的潜在方法,该方法使用具有很少检测到的光事件的图像执行模式识别。以前使用强度图像进行的工作显示低至50次光电检测就可以接近理想的模式识别。我们通过仿真研究了先前工作对3D点云图像的扩展。

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