Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)

摘要

This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous trajectory estimation and mapping (STEAM), a variant of SLAM.