Trajectory comparison improved by curvature based time alignment
Comparing Trajectories from Positions Alone: Curvature-Based Time Alignment and Drift Error Metric
Robotics
Summary
Comparing robot movement paths accurately is hard because existing methods rely on some hidden assumptions and parameters that aren’t always clear. The authors introduce a new way to line up different robot paths in time using a shape-based feature called curvature, which helps make comparisons fairer and more reliable. They also propose a new error measurement that adjusts for how far the robot traveled. Their approach makes it easier to check how well robot navigation and mapping systems work by considering timing, sampling, and calibration differences.
What this means in practice
- •For field robotics engineers: Evaluate and compare robot position estimates more accurately by aligning trajectories based on curvature signals.
- •For autonomous vehicle developers: Improve localization and mapping system testing by normalizing error metrics with traveled distance and aligning timing explicitly.
Authors
Effie Daum, Daniele De Martini, Claire Dune, François Pomerleau
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.