Abstract
Robotic Template Library (RTL) is a set of tools for dealing with geometry and point cloud processing, especially in robotic applications. The software package covers basic objects such as vectors, line segments, quaternions, rigid transformations, etc., however, its main contribution lies in the more advanced modules: The segmentation module for batch or stream clustering of point clouds, the fast vectorization module for approximation of continuous point clouds by geometric objects of higher grade and the LaTeX export module enabling automated generation of high-quality visual outputs. It is a header-only library written in C++17, uses the Eigen library as a linear algebra back-end, and is designed with high computational performance in mind. RTL can be used in all robotic tasks such as motion planning, map building, object recognition and many others, but the point cloud processing utilities are general enough to be employed in any field touching object reconstruction and computer vision applications as well.
Highlights
Robotic Template Library (RTL) development started together with our research of the fast vectorization algorithms and is a repository of code we used to illustrate our results in research papers
RTL provides an implementation of the fast total least squares (FTLS) vectorization [11] of the ordered point clouds – an optimized algorithm for approximation of ordered data, which provides computational performance similar to the point-eliminating methods [12], while preserving all the benefits of the TLS regression
From the user’s point of view, the tests are useful for examination whether changes to the library did not broke the rest of the code and can serve as a companion to the examples, since they cover RTL more completely
Summary
Robotic Template Library (RTL) development started together with our research of the fast vectorization algorithms and is a repository of code we used to illustrate our results in research papers. RTL provides an implementation of the fast total least squares (FTLS) vectorization [11] of the ordered point clouds – an optimized algorithm for approximation of ordered data, which provides computational performance similar to the point-eliminating methods [12], while preserving all the benefits of the TLS regression.
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