A self-supervised video-only pipeline with LiDAR-generated pseudo-labels and a Vision-Mamba network predicts UAV 3D trajectories up to 5 seconds ahead on the MMAUD dataset.
A Survey on Event-based Optical Marker Systems
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abstract
The advent of event-based cameras, with their low latency, high dynamic range, and reduced power consumption, marked a turning point in machine perception and robotic vision. In~particular, the combination of these neuromorphic sensors with widely-available passive or active optical markers (e.g. AprilTags, arrays of blinking LEDs), has recently opened up a new field of opportunities. This survey paper provides a comprehensive review of Event-Based Optical Marker Systems (EBOMS). We~analyze the underlying principles and technologies on which these systems are based, with a special focus on their asynchronous operation and robustness against challenging lighting conditions. We also describe the most relevant applications of EBOMS, including object detection and tracking, pose estimation, and optical communication. The article concludes with a discussion of possible future research directions in this rapidly-emerging and multidisciplinary area.
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Label-Free Long-Horizon 3D UAV Trajectory Prediction via Motion-Aligned RGB and Event Cues
A self-supervised video-only pipeline with LiDAR-generated pseudo-labels and a Vision-Mamba network predicts UAV 3D trajectories up to 5 seconds ahead on the MMAUD dataset.