水下视觉目标跟踪：目标特定深度估计与自适应模型融合预测控制
Underwater Visual Target Tracking with Target-Specific Depth Estimation and Adaptive Model-Fusion Predictive Control
- Shanghai Jiao Tong University（上海交通大学）
- University of Toronto（多伦多大学）
- University of Calgary（卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于立体视觉伺服的水下目标跟踪框架，通过目标特定深度提取与卡尔曼滤波实现稳定感知，并采用自适应模型融合预测控制实现实时平移控制，仿真和实验验证其性能优于现有方法。
AI中文摘要：
基于视觉的水下目标跟踪面临深度测量不可靠和目标运动未知的挑战。本文提出了一种用于自主水下航行器（AUV）的立体视觉伺服框架。在感知方面，该框架通过目标特定深度提取和卡尔曼滤波，从立体图像中推导出稳定的3D相对状态。它利用颜色、视差和时间线索构建目标深度掩码，以选择可靠的目标像素，然后分别对所得的深度测量值和检测到的图像中心进行滤波。在控制方面，该框架将偏航调节与平移控制解耦，避免了计算量大的耦合多自由度优化，并实现了实时平移模型预测控制（MPC）。平移控制器采用自适应模型融合预测控制，结合恒速和零速目标模型以适应不同的目标运动模式。它利用历史预测误差更新模型权重，并在执行器、跟随距离和视场约束下计算平移指令。通过仿真和实际实验，我们验证了所提出框架的有效性，并表明其性能优于现有框架。
英文摘要：
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.
