NightSight: Passive Computation for Navigation in Dark Using Events
NightSight presents a lightweight perception approach combining a monocular event camera, coded aperture lens, and IR dot projector to enable autonomous navigation in complete darkness for small aerial robots. The system uses depth-dependent blur from the coded aperture to train a CNN on synthetic data, achieving zero-shot generalization to real scenes. It runs at 20 Hz on an NVIDIA Jetson Orin Nano with 7.0 cm error up to 2.5 m range.
Article intelligence
Key points
- Combines event camera, coded aperture, and IR projection for passive depth sensing in darkness
- CNN trained solely on synthetic data generalizes zero-shot to complex real-world scenes
- Real-time operation at 20 Hz on low-power hardware with centimeter-level accuracy
- Enables dark navigation for resource-constrained small aerial robots
Why it matters
This matters because combines event camera, coded aperture, and IR projection for passive depth sensing in darkness.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26330] NightSight: Passive Computation for Navigation in Dark Using Events
[Submitted on 25 May 2026]
Title:NightSight: Passive Computation for Navigation in Dark Using Events
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Abstract:Small aerial robots are particularly well-suited for search and rescue in confined and hazardous environments due to their agility, low cost, and ability to traverse through cluttered spaces that are inaccessible to larger platforms. However, enabling autonomous navigation in complete darkness remains a significant challenge, because small aerial robots cannot easily accommodate perception systems that demand substantial payload, power, or computation. In this work, we present a lightweight perception approach that combines a monocular event camera, a coded aperture lens, and an infrared dot projector to enable navigation in such conditions. The projected pattern, when imaged through the coded aperture, produces depth dependent blur signatures that implicitly encode scene geometry. We train a convolutional neural network to decode these signatures into dense depth maps using only synthetic data generated from a simple planar wall setup. Despite this minimal training regime, the model generalizes zero-shot to complex real-world scenes. Our system operates in real time at 20 Hz on a NVIDIA Jetson Orin Nano, demonstrating suitability for resource-constrained platforms. We further analyze the impact of different coded aperture designs on depth estimation performance. Our approach gives high accuracy (l1 error 7.0cm) upto 2.5m range (2.80% error). These results highlight the potential of combining structured illumination, coded optics, and event-based sensing for enabling robust perception and navigation in complete darkness.
Comments: 6 pages, 7 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.26330 [cs.RO]
(or arXiv:2605.26330v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.26330
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Deepak Singh [view email] [v1] Mon, 25 May 2026 21:07:44 UTC (22,039 KB)
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