  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.
