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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Sensor Fusion for Intelligent Behavior on Small Unmanned Ground Vehicles
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Sensor Fusion for Intelligent Behavior on Small Unmanned Ground Vehicles

机译：小型无人地面车辆的智能行为传感器融合

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摘要

Sensors commonly mounted on small unmanned ground vehicles (UGVs) include visible light and thermal cameras, scanning LIDAR, and ranging sonar. Sensor data from these sensors is vital to emerging autonomous robotic behaviors. However, sensor data from any given sensor can become noisy or erroneous under a range of conditions, reducing the reliability of autonomous operations. We seek to increase this reliability through data fusion. Data fusion includes characterizing the strengths and weaknesses of each sensor modality and combining their data in a way such that the result of the data fusion provides more accurate data than any single sensor. We describe data fusion efforts applied to two autonomous behaviors: leader-follower and human presence detection. The behaviors are implemented and tested in a variety of realistic conditions.
机译：通常安装在小型无人机上的传感器包括可见光和热像仪，扫描激光雷达和测距声纳。这些传感器的传感器数据对于新兴的自主机器人行为至关重要。但是，来自任何给定传感器的传感器数据在一定条件下会变得嘈杂或错误，从而降低了自主操作的可靠性。我们寻求通过数据融合来提高这种可靠性。数据融合包括表征每种传感器模式的优缺点，并以某种方式组合它们的数据，使得数据融合的结果提供比任何单个传感器更准确的数据。我们描述了应用于两种自主行为的数据融合工作：领导者跟随者和人类存在检测。这些行为是在各种现实条件下实现和测试的。

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