AMR-Pose: An Active LED Marker-Based Relative Pose Estimation Framework With Probabilistic Switching PnP for Cooperative AUVs

2026-08-13

AI Summary

This work addresses the unreliability of visual relative pose estimation among multiple autonomous underwater vehicles (AUVs) caused by underwater turbidity, illumination variations, and feature occlusions. To overcome these challenges, the authors propose a robust six-degree-of-freedom estimation framework leveraging active red-blue LED markers. The approach integrates SE(3) Lie group-based pose propagation, probabilistic marker association, and visibility-adaptive fusion, and introduces a novel Probabilistic Switching Perspective-n-Point (PSwPnP) algorithm that dynamically selects solving strategies based on marker visibility to ensure geometric consistency and temporal stability. Pool experiments demonstrate that the framework achieves high-precision, smooth, and robust relative pose estimation in complex underwater environments, and its effectiveness in real-time multi-AUV cooperative systems is validated through closed-loop leader-follower missions.

Abstract

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.

Problem

• relative pose estimation
• underwater vision
• autonomous underwater vehicles
• optical degradation
• cooperative robotics

Innovation

• active LED marker
• probabilistic switching PnP
• relative pose estimation
• cooperative AUVs
• underwater vision