无需定制化设计，即可实现多传感器融合的机器人全局定位与状态估计。
Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs
- 将多种测量值作为状态变量纳入优化，通过随机游走建模其变化。
- 在典型机器人硬件上实现低延迟、低漂移的实时状态估计。
- 适用于不同任务和硬件平台，开箱即用，适合实际部署场景。
移动机器人在复杂环境中无缝运行需要低延迟的局部运动估计和高精度的全局定位。现有传感器融合方法多针对特定场景设计，本文提出一种灵活开源的通用解决方案——全链路融合（Holistic Fusion），支持任务与配置无关的多模态传感器融合。该方法将传感器融合建模为两部分联合估计：一是机器人的局部与全局状态，二是理论上数量不限的动态变量，包括参考系自动对齐。通过显式将各类测量值（绝对、局部、地标）作为状态纳入优化，并以随机游走方式建模其演化，可直接融合来自不同参考帧的任意数量测量。同时特别关注局部平滑性与一致性，防止估计跳跃。该框架可在典型机器人硬件上实现低延迟、低漂移的在线状态估计，且保持与IMU采样率一致的全局定位精度。在三个不同平台的五个真实场景中验证了其有效性，展示了融合多种绝对测量类型的优势。代码已开源：https://github.com/leggedrobotics/holistic_fusion，项目主页：https://leggedrobotics.github.io/holistic_fusion。
原文摘要 · Abstract (English)
Seamless operation of mobile robots in challenging environments requires low-latency local motion estimation and accurate global localization. While most sensor-fusion approaches are designed for specific scenarios, this work introduces a flexible open-source solution for task- and setup-agnostic multimodal sensor fusion distinguished by its generality and usability. Holistic Fusion formulates sensor fusion as a combined estimation problem of i) the local and global robot state and ii) a (theoretically unlimited) number of dynamic variables, including automatic alignment of reference frames; this formulation fits countless real-world applications without conceptual modifications, offering a comprehensive solution beyond hard-coded/task-specific approaches. The proposed factor-graph formulation enables direct fusion of an arbitrary number of absolute, local, and landmark measurements expressed with respect to different frames by explicitly including them as states in the optimization and modeling their evolution as random walks. Moreover, local smoothness and consistency receive particular attention to prevent estimation jumps. Holistic Fusion enables low-latency and smooth online state estimation on typical robot hardware while simultaneously providing low-drift global localization at the IMU measurement rate. The efficacy of this released framework [1] is demonstrated in five real-world scenarios on three robotic platforms with distinct task requirements, highlighting the advantages of fusing multiple absolute measurement types [2]. [1] Code: https://github.com/leggedrobotics/holistic_fusion [2] Project: https://leggedrobotics.github.io/holistic_fusion
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。