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Object recognition using low light level 3D point clouds

机译:使用低光级3D点云的对象识别

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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。快速熟化的小型传感器平台不断需要更好,更敏捷的传感器性能,耦合较小,更轻,更快的传感器实现。另外,更长的范围应用需要更有效地使用从主动照明接收的光子。我们描述了一种基于光子计数激光雷达克服了两个问题的潜在方法,其使用具有极少检测到的照片事件的图像进行模式识别。以前的工作使用强度图像显示在理想的模式识别附近,具有低至50个照片检测。我们通过模拟对3D点云图像的延伸来调查。

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