Active Illumination for Visual Ego-Motion Estimation in the Dark

TL;DR

This paper introduces an active illumination system that dynamically directs light to enhance visual feature detection in dark environments, significantly improving the accuracy of visual ego-motion estimation.

Contribution

It presents a novel framework combining deep learning and active lighting control to improve VO and V-SLAM in low-light conditions, which was not addressed before.

Findings

Pose estimation error reduced by up to 75%.

Enhanced feature detection in dark environments.

Effective real-time implementation on a robotic platform.

Abstract

Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approach dynamically controls a moving light source to illuminate highly textured areas, thereby improving feature extraction and tracking. Specifically, a detector block, which incorporates a deep learning-based enhancing network, identifies regions with relevant features. Then, a pan-tilt controller is responsible for guiding the light beam toward these areas, so that to provide information-rich images to the ego-motion estimation algorithm. Experimental results on a real robotic platform demonstrate the effectiveness of the proposed method, showing a reduction in the pose estimation error up to 75% with respect to a traditional fixed lighting technique.