ROCVMar 29, 2021

A tutorial on $\mathbf{SE}(3)$ transformation parameterizations and on-manifold optimization

arXiv:2103.15980v26 citations
AI Analysis

It provides a unified reference for engineers and researchers working on robotics and computer vision, but it is incremental as it consolidates existing knowledge without introducing new methods.

This tutorial reviews three common representations of rigid transformations in SE(3) and their interconversions, compositions, and uncertainty propagation, with validated implementations in the MRPT C++ library.

An arbitrary rigid transformation in $\mathbf{SE}(3)$ can be separated into two parts, namely, a translation and a rigid rotation. This technical report reviews, under a unifying viewpoint, three common alternatives to representing the rotation part: sets of three (yaw-pitch-roll) Euler angles, orthogonal rotation matrices from $\mathbf{SO}(3)$ and quaternions. It will be described: (i) the equivalence between these representations and the formulas for transforming one to each other (in all cases considering the translational and rotational parts as a whole), (ii) how to compose poses with poses and poses with points in each representation and (iii) how the uncertainty of the poses (when modeled as Gaussian distributions) is affected by these transformations and compositions. Some brief notes are also given about the Jacobians required to implement least-squares optimization on manifolds, an very promising approach in recent engineering literature. The text reflects which MRPT C++ library functions implement each of the described algorithms. All formulas and their implementation have been thoroughly validated by means of unit testing and numerical estimation of the Jacobians

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