Problem
Research questions and friction points this paper is trying to address.
trajectory evaluation
field robotics
absolute trajectory error
relative pose error
curvature
Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric
🤖 AI Summary
本文提出了一种基于曲率信号的时间对齐方法和一种按行驶距离归一化的误差度量，以解决大规模参考轨迹获取难题并提高评估准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
curvature-based time alignment
drift error metric
trajectory evaluation
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