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This paper introduces AMR-Pose, a novel framework for relative pose estimation in cooperative autonomous underwater vehicles (AUVs) that utilizes an active LED marker system to enhance visibility in challenging underwater environments. By integrating a compact marker module with a probabilistic switching Perspective-n-Point (PSwPnP) estimator, the framework effectively addresses issues of optical degradation and occlusion, ensuring robust six-degree-of-freedom pose estimation. Experimental results demonstrate that AMR-Pose achieves accurate and stable relative localization, even under conditions of partial visibility, making it suitable for real-time applications in underwater robotics.
Active LED markers enable AUVs to maintain precise relative localization even in murky underwater conditions, overcoming traditional vision-based limitations.
Reliable relative pose estimation between autonomous underwater vehicles (AUVs) is critical for cooperative ocean exploration, sampling, and multi-robot coordination. However, achieving robust vision-based relative localization in underwater environments remains challenging due to severe optical degradation, including turbidity, illumination variations, reflections, and intermittent feature occlusions. This paper presents AMR-Pose, an active LED marker-based relative pose estimation framework for cooperative AUVs. A compact marker module consisting of one red central LED and three blue peripheral LEDs is developed and integrated onto the leader AUV to provide distinctive visual features under complex underwater conditions. Building upon the detected marker observations, a probabilistic switching Perspective-n-Point estimator (PSwPnP) is developed by combining Lie-group pose propagation on $SE(3)$, probabilistic marker association, and visibility-adaptive measurement fusion for robust six-degree-of-freedom relative pose estimation. The proposed framework dynamically adapts the estimation process according to marker visibility, maintaining geometric consistency and temporal stability during partial observations and visibility transitions. Extensive water-tank experiments with motion-capture ground truth validate that AMR-Pose achieves accurate, smooth, and robust relative pose estimation under challenging underwater conditions. Closed-loop leader-follower experiments further demonstrate its feasibility for real-time relative pose feedback in cooperative underwater robotics.
