Underwater Visual Target Tracking with Target-Specific Depth Estimation and Adaptive Model-Fusion Predictive Control

2026-09-17

AI Summary

本文针对水下视觉目标跟踪中的深度测量不可靠和目标运动未知问题，提出了一种基于特定目标深度估计和自适应模型融合预测控制的立体视觉伺服框架。

Abstract

Vision-based underwater target tracking is challenged by unreliable depth measurements and unknown target motion. This paper proposes a stereo visual-servoing framework for an autonomous underwater vehicle (AUV). For perception, the framework derives a stable 3D relative state from stereo images through target-specific depth extraction and Kalman filtering. It constructs a target-depth mask from color, disparity, and temporal cues to select reliable target pixels, and then filters the resulting depth measurement and detected image center separately. For control, the framework decouples yaw regulation from translational control, avoiding computationally expensive coupled multi-DOF optimization and enabling real-time translational MPC. The translational controller employs adaptive model-fusion predictive control, combining constant-velocity and zero-velocity target models to accommodate different target-motion patterns. It updates the model weights using historical prediction errors and computes translational commands subject to actuation, following-distance, and field-of-view constraints. Through simulations and real-world experiments, we validate the effectiveness of the proposed framework and show it has better performance than existing frameworks.

Problem

• underwater target tracking
• unreliable depth measurements
• unknown target motion

Innovation

• stereo visual-servoing
• target-specific depth extraction
• adaptive model-fusion predictive control
• real-time translational MPC