On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events

• URL: http://arxiv.org/abs/2412.06359v2
• Date: Tue, 25 Mar 2025 10:43:50 GMT
• Title: On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events
• Authors: Jesse Hagenaars, Yilun Wu, Federico Paredes-Vallés, Stein Stroobants, Guido de Croon,
• Abstract summary: Event cameras provide low-latency perception for only milliwatts of power.<n>Self-supervised learning based on contrast holds great potential for event-based robot vision.<n>Online learning raises the challenge of achieving sufficient computational efficiency for real-time learning.
• Score: 10.609097572690438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-supervised learning based on contrast maximization holds great potential for event-based robot vision, as it foregoes the need for high-frequency ground truth and allows for online learning in the robot's operational environment. However, online, on-board learning raises the major challenge of achieving sufficient computational efficiency for real-time learning, while maintaining competitive visual perception performance. In this work, we improve the time and memory efficiency of the contrast maximization pipeline, making on-device learning of low-latency monocular depth possible. We demonstrate that online learning on board a small drone yields more accurate depth estimates and more successful obstacle avoidance behavior compared to only pre-training. Benchmarking experiments show that the proposed pipeline is not only efficient, but also achieves state-of-the-art depth estimation performance among self-supervised approaches. Our work taps into the unused potential of online, on-device robot learning, promising smaller reality gaps and better performance.

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