A Spatiotemporal Hand-Eye Calibration for Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

TL;DR

This paper introduces a novel spatiotemporal hand-eye calibration method that improves trajectory alignment accuracy in visual(-inertial) odometry evaluation by leveraging multiple constraints for robustness.

Contribution

The paper presents a new calibration algorithm that accounts for noise and drift, enhancing accuracy and robustness over traditional methods.

Findings

Better performance than state-of-the-art methods

Less noise-prone in trajectory alignment

Improves evaluation accuracy of VO/VIO systems

Abstract

A common prerequisite for evaluating a visual(-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a motion capture system. The trajectory-based alignment, typically modeled as a classic hand-eye calibration, significantly influences the accuracy of evaluation metrics. However, traditional calibration methods are susceptible to the quality of the input poses. Few studies have taken this into account when evaluating VO/VIO trajectories that usually suffer from noise and drift. To fill this gap, we propose a novel spatiotemporal hand-eye calibration algorithm that fully leverages multiple constraints from screw theory for enhanced accuracy and robustness. Experimental results show that our algorithm has better performance and is less noise-prone than state-of-the-art methods.