对比三种四足机器人状态估计算法的精度与速度，指导实际应用选型。
A Proprioceptive-Only Benchmark for Quadruped State Estimation: ATE, RPE, and Runtime Trade-offs Between Filters and Smoothers
- 在固定硬件上比较滤波器与平滑器的实时表现
- IEKF和IS比MUSE更低的轨迹误差（ATE）
- 开源代码支持复现，适合工程部署参考
我们在GrandTour数据集的CYN-1序列上，对比了三种先进的仅自感知状态估计算法：MUSE、不变扩展卡尔曼滤波器（IEKF）和不变平滑器（IS）。目标是为实践者提供准确性和计算时间的清晰指导。报告了长期精度（绝对轨迹误差，ATE）、短期精度（平移与旋转相对位姿误差，RPE）以及固定软硬件平台下的单次更新计算时间。结果显示，各方法的RPE表现相近，而IEKF与IS的ATE显著低于MUSE。运行时结果揭示了不同方法在精度与延迟之间的权衡。讨论部分阐述了确保公平比较的评估设计，并分析影响短时指标的因素。本研究提供了精度与成本的简洁快照，帮助读者根据应用需求选择合适算法。所有评估代码与文档已开源，详见https://github.com/iit-DLSLab/state_estimation_benchmark。
原文摘要 · Abstract (English)
We compare three state-of-the-art proprioceptive state estimators for quadruped robots: MUSE [1], the Invariant Extended Kalman Filter (IEKF) [2], and the Invariant Smoother (IS) [3], on the CYN-1 sequence of the GrandTour Dataset [4]. Our goal is to give practitioners clear guidance on accuracy and computation time: we report long-term accuracy (Absolute Trajectory Error, ATE), short-term accuracy (translational and rotational Relative Pose Error, RPE), and per-update computation time on a fixed hardware/software stack. On this dataset, RPEs are broadly similar across methods, while IEKF and IS achieve a lower ATE than MUSE. Runtime results highlight the accuracy-latency trade-offs across the three approaches. In the discussion, we outline the evaluation choices used to ensure a fair comparison and analyze factors that influence short-horizon metrics. Overall, this study provides a concise snapshot of accuracy and cost, helping readers choose an estimator that fits their application constraints, with all evaluation code and documentation released open-source at https://github.com/iit-DLSLab/state_estimation_benchmark for full reproducibility.
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