Accurate localization is fundamental for autonomous underwater vehicles
(AUVs) to carry out precise tasks, such as manipulation and construction.
Vision-based solutions using fiducial marker are promising, but extremely
challenging underwater because of harsh lighting condition underwater. This
paper introduces a gradient-based active camera exposure control method to
tackle sharp lighting variations during image acquisition, which can establish
better foundation for subsequent image enhancement procedures. Considering a
typical scenario for underwater operations where visual tags are used, we
proposed several experiments comparing our method with other state-of-the-art
exposure control method including Active Exposure Control (AEC) and
Gradient-based Exposure Control (GEC). Results show a significant improvement
in the accuracy of robot localization. This method is an important component
that can be used in visual-based state estimation pipeline to improve the
overall localization accuracy.

准确的本地化对于自主水下载具（AUVs）进行精确任务非常重要，而对于水下恶劣的照明条件，使用基于视觉标记的基于图像梯度的主动摄像机曝光控制方法可以显著提高机器人定位的准确性。

应用自适应主动曝光控制技术改善现场视觉基准标志的感知

Improving the perception of visual fiducial markers in the field using  Adaptive Active Exposure Control

Current fiducial marker detection algorithms rely on marker IDs for false
positive rejection. Time is wasted on potential detections that will eventually
be rejected as false positives. We introduce ChromaTag, a fiducial marker and
detection algorithm designed to use opponent colors to limit and quickly reject
initial false detections and grayscale for precise localization. Through
experiments, we show that ChromaTag is significantly faster than current
fiducial markers while achieving similar or better detection accuracy. We also
show how tag size and viewing direction effect detection accuracy. Our
contribution is significant because fiducial markers are often used in
real-time applications (e.g. marker assisted robot navigation) where heavy
computation is required by other parts of the system.

本文提出一种利用对比颜色来减少误检测、采用灰度来精确定位的标记检测算法 ChromaTag。通过实验发现 ChromaTag 比现有标记算法更快、准确度相似或更高，对实时应用具有重要意义。