Underwater environments impose severe challenges to visual-inertial odometry systems, as strong light attenuation, marine snow and turbidity, together with weakly exciting motions, degrade inertial observability and cause frequent tracking failures over long-term operation. While tightly coupled acoustic-visual-inertial fusion, typically implemented through an acoustic Doppler Velocity Log (DVL) integrated with visual-inertial measurements, can provide accurate state estimation, the associated graph-based optimization is often computationally prohibitive for real-time deployment on resource-constrained platforms. Here we present FAR-AVIO, a Schur-Complement based, tightly coupled acoustic-visual-inertial odometry framework tailored for underwater robots. FAR-AVIO embeds a Schur complement formulation into an Extended Kalman Filter(EKF), enabling joint pose-landmark optimization for accuracy while maintaining constant-time updates by efficiently marginalizing landmark states. On top of this backbone, we introduce Adaptive Weight Adjustment and Reliability Evaluation(AWARE), an online sensor health module that continuously assesses the reliability of visual, inertial and DVL measurements and adaptively regulates their sigma weights, and we develop an efficient online calibration scheme that jointly estimates DVL-IMU extrinsics, without dedicated calibration manoeuvres. Numerical simulations and real-world underwater experiments consistently show that FAR-AVIO outperforms state-of-the-art underwater SLAM baselines in both localization accuracy and computational efficiency, enabling robust operation on low-power embedded platforms. Our implementation has been released as open source software at https://far-vido.gitbook.io/far-vido-docs.

翻译：水下环境对视觉-惯性里程计系统提出了严峻挑战，强烈的光衰减、海雪与浑浊度，以及激励微弱的运动，共同降低了惯性可观测性，并导致长期运行中频繁出现跟踪失败。虽然紧耦合的声学-视觉-惯性融合（通常通过将声学多普勒速度计程仪与视觉-惯性测量相结合来实现）能够提供精确的状态估计，但相关的基于图的优化计算量往往过大，难以在资源受限的平台上实时部署。本文提出FAR-AVIO，一种专为水下机器人设计的、基于舒尔补的紧耦合声学-视觉-惯性里程计框架。FAR-AVIO将舒尔补公式嵌入扩展卡尔曼滤波器，通过高效边缘化路标状态，在实现联合位姿-路标优化以保障精度的同时，保持了恒定时间更新。在此框架基础上，我们引入了自适应权重调整与可靠性评估模块，这是一个在线传感器健康监测模块，持续评估视觉、惯性及DVL测量的可靠性并自适应地调节其Sigma权重；同时，我们开发了一种高效的在线标定方案，能够联合估计DVL-IMU外参，而无需专门的标定机动。数值仿真与真实水下实验一致表明，FAR-AVIO在定位精度与计算效率上均优于当前最先进的水下SLAM基线方法，使其能够在低功耗嵌入式平台上实现鲁棒运行。我们的实现已作为开源软件发布于 https://far-vido.gitbook.io/far-vido-docs。