仅从位置比较轨迹：基于曲率的时间对齐与漂移误差度量
Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric
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中文总结 AI 辅助
本文提出一种基于曲率时间对齐和距离归一化误差度量的轨迹评估协议，以解决现场机器人中参考轨迹获取难题，提升状态估计、定位和SLAM评估的标准化与可靠性。
中文摘要 AI 辅助
在现场机器人学中，获取比被评估估计值更精确的独立大规模参考轨迹仍然是一个未解决的挑战。该领域广泛依赖绝对轨迹误差（ATE）和相对位姿误差（RPE），这些误差通过自动化工具计算，而这些工具基于的假设和评估参数很少被明确说明。当这些参数未被报告时，误差可能具有误导性并阻碍公平比较。本文提出了一种用于状态估计、定位和同步定位与建图（SLAM）中标准化且可靠的精度评估的轨迹评估协议。该方法将一种基于曲率信号的新型时间对齐方法与按行驶距离归一化的误差度量相结合。我们明确考虑了时间同步、采样对齐和外部标定，并通过敏感性分析量化了它们的影响。所提出的协议有助于更严格、可重复和标准化的轨迹评估。
英文摘要
In field robotics, acquiring independent large-scale reference trajectories more accurate than the evaluated estimates remains an open challenge. The domain is widely reliant on Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), computed with automated tools, that rest on assumptions and evaluation parameters rarely made explicit. When unreported, the errors can be misleading and hinder fair comparisons. This paper introduces a trajectory-evaluation protocol for standardized and reliable accuracy assessment in state estimation, localization, and Simultaneous Localization And Mapping (SLAM). The approach combines a novel temporal alignment method based on curvature signals with an error metric normalized by travelled distance. We explicitly account for temporal synchronization, sampling alignment, and extrinsic calibration, quantifying their influence through a sensitivity analysis. The proposed protocol contributes to more rigorous, reproducible, and standardized trajectory evaluation.
发表机构
- Université Laval（拉瓦尔大学）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
