Continuous-time Trajectory Estimation: A Comparative Study Between Gaussian Process and Spline-based Approaches
CoRR(2024)
摘要
Continuous-time trajectory estimation is an attractive alternative to
discrete-time batch estimation due to the ability to incorporate high-frequency
measurements from asynchronous sensors while keeping the number of optimization
parameters bounded. Two types of continuous-time estimation have become
prevalent in the literature: Gaussian process regression and spline-based
estimation. In this paper, we present a direct comparison between these two
methods. We first compare them using a simple linear system, and then compare
them in a camera and IMU sensor fusion scenario on SE(3) in both simulation and
hardware. Our results show that if the same measurements and motion model are
used, the two methods achieve similar trajectory accuracy. In addition, if the
spline order is chosen so that the degree-of-differentiability of the two
trajectory representations match, then they achieve similar solve times as
well.
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