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This research addresses critical evaluation challenges in field robotics by introducing a curvature-based temporal alignment method and a distance-normalized error metric to replace opaque standard practices. By explicitly accounting for synchronization and calibration issues, the protocol aims to enable more rigorous and reproducible accuracy assessments for state estimation and SLAM systems. While the work is rooted in robotics (cs.RO), its focus on improving the mathematical rigor of trajectory evaluation directly supports the development of robust autonomous agents relying on advanced perception and learning algorithms.
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Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric

arXiv2 min read

arXiv:2609.14936v1 Announce Type: new 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.

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.

Robotics (cs.RO)

arXiv:2609.14936 [cs.RO]

(or arXiv:2609.14936v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.14936 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 223 words

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