LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light

TL;DR

This paper introduces LBurst, a learning-based feature extraction method that significantly improves 3D reconstruction quality in low-light conditions for drone applications, enabling effective nighttime and underground operations.

Contribution

LBurst is a novel learning architecture that detects high-quality features in low-light images, enhancing 3D reconstruction in challenging environments.

Findings

Effective in millilux illumination conditions

Improves feature quality in low signal-to-noise images

Enables drone operation in night and underground scenarios

Abstract

Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Our approach enhances visual reconstruction by detecting and describing high quality true features and less spurious features in low signal-to-noise ratio images. We demonstrate that our method is capable of handling challenging scenes in millilux illumination, making it a significant step towards drones operating at night and in extremely low-light applications such as underground mining and search and rescue operations.