Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric
Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric
This research introduces a standardized protocol for evaluating trajectory accuracy in field robotics, addressing the limitations of common metrics like Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The authors propose a novel method that aligns trajectories based on curvature signals rather than just positions, combined with an error metric normalized by traveled distance. The approach explicitly accounts for temporal synchronization, sampling alignment, and extrinsic calibration, providing a more rigorous and reproducible framework for state estimation, localization, and SLAM evaluation.
The paper proposes a new trajectory evaluation protocol using curvature-based time alignment and a distance-normalized error metric to address limitations in standard Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) assessments.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Robot trajectoriesOpen source S2
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
arXiv:2609.14936v2 Announce Type: replace 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 er
Open source S2
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Match the buyer’s robot embodiment, action space, sensors and timestamp requirements before planning collection.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org