面向自适应与成本感知视觉惯性里程计的双智能体强化学习
Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry
用轻量强化学习智能体动态决定视觉前端是否运行以及融合权重，为资源受限平台上的视觉惯性里程计提供了一种可复用的计算调度思路。
原文依据 we cast two key design choices in modern VIO, when to run the visual frontend and how strongly to trust its output, as sequential decision problems
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉惯性里程计（VIO）是鲁棒自运动估计的关键组件，为机器人自主导航和增强现实中的实时6自由度跟踪等基础能力提供支持。现有方法面临一个众所周知的权衡：基于滤波器的方法高效但容易漂移，而基于优化的方法虽然准确，却依赖于计算成本高昂的视觉惯性光束法平差（VIBA），难以在资源受限的平台上运行。我们并非完全移除VIBA，而是旨在降低其被调用的频率和强度。为此，我们将现代VIO中的两个关键设计选择——何时运行视觉前端以及对其输出的信任程度——建模为序列决策问题，并用轻量级强化学习（RL）智能体求解。我们的框架引入了一种轻量级双分支RL策略作为核心贡献：（1）选择智能体仅基于高频IMU数据智能地门控整个VO流水线；（2）复合融合智能体首先通过监督网络估计鲁棒的速度状态，然后由RL策略自适应地融合完整的（p, v, q）状态。在EuRoC MAV和TUM-VI数据集上的实验表明，在我们的统一评估中，所提方法比先前的基于GPU的VO/VIO系统实现了更有利的精度-效率-内存权衡：它达到了最佳平均ATE，同时运行速度最高提升1.77倍且使用更少的GPU内存。与经典的基于优化的VIO系统相比，我们的方法在保持有竞争力的轨迹精度的同时大幅降低了计算负载。
英文原文摘要 1663 字符
Visual-Inertial Odometry (VIO) is a critical component for robust ego-motion estimation, enabling foundational capabilities such as autonomous navigation in robotics and real-time 6-DoF tracking for augmented reality.Existing methods face a well-known trade-off: filter-based approaches are efficient but prone to drift, while optimization-based methods, though accurate, rely on computationally prohibitive Visual-Inertial Bundle Adjustment (VIBA) that is difficult to run on resource-constrained platforms.Rather than removing VIBA altogether, we aim to reduce how often and how heavily it must be invoked. To this end, we cast two key design choices in modern VIO, when to run the visual frontend and how strongly to trust its output, as sequential decision problems, and solve them with lightweight reinforcement learning (RL) agents. Our framework introduces a lightweight, dual-pronged RL policy that serves as our core contribution: (1) a Select Agent intelligently gates the entire VO pipeline based only on high-frequency IMU data; and (2) a composite Fusion Agent that first estimates a robust velocity state via a supervised network , before an RL policy adaptively fuses the full (p, v, q) state.Experiments on the EuRoC MAV and TUM-VI datasets show that, in our unified evaluation, the proposed method achieves a more favorable accuracy-efficiency-memory trade-off than prior GPU-based VO/VIO systems: it attains the best average ATE while running up to 1.77xfaster and using less GPU memory. Compared to classical optimization-based VIO systems, our approach maintains competitive trajectory accuracy while substantially reducing computational load.