FAR-AVIO：基于Schur补的快速且稳健的声学-视觉-惯性融合里程计
FAR-AVIO: Fast and Robust Schur-Complement Based Acoustic-Visual-Inertial Fusion Odometry with Sensor Calibration
AI总结：
FAR-AVIO通过基于Schur补的EKF实现快速且稳健的水下声学-视觉-惯性融合里程计，结合自适应权重调整和在线校准，提升定位精度与计算效率。
摘要：
水下环境对视觉-惯性里程计系统提出了严峻挑战，因为强光衰减、海水雪和浑浊，加上弱激励运动，会降低惯性可观察性，并导致长时间运行中频繁出现跟踪失败。尽管紧密耦合的声学-视觉-惯性融合通常通过声学多普勒速度计（DVL）与视觉-惯性测量相结合来实现，可以提供准确的状态估计，但相关的基于图的优化通常对实时部署在资源受限平台上计算上是不可行的。在这里，我们提出了FAR-AVIO，一种基于Schur补的紧密耦合声学-视觉-惯性里程计框架，专为水下机器人设计。FAR-AVIO将Schur补公式嵌入扩展卡尔曼滤波器（EKF），实现姿态-地标联合优化以提高精度，同时通过高效边缘化地标状态保持恒定时间更新。在此基础上，我们引入了自适应权重调整和可靠性评估（AWARE），一个在线传感器健康模块，能够持续评估视觉、惯性和DVL测量的可靠性，并自适应调节它们的sigma权重；我们还开发了一种高效的在线校准方案，无需专门的校准操作即可联合估计DVL-IMU外参。数值模拟和真实水下实验一致表明，FAR-AVIO在定位精度和计算效率方面均优于最先进的水下SLAM基线，使其能够在低功耗嵌入式平台上实现稳健运行。我们的实现已作为开源软件发布在https://far-vido.gitbook.io/far-vido-docs。
英文摘要：
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.
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