This group contains documentation useful for working with Nuklei (as opposed to contributing to Nuklei). More...
Modules | |
| Background | |
| Nuklei was initially designed to model density functions defined on the Special Euclidean group \( SE(3) \) (or on a subspace thereof). This page reviews the theory behind density estimation and density integrals particularized to \( SE(3) \) data. This is the place to start if you haven't yet heard of nonparametric representations, quaternions, or von Mises-Fisher distributions. Once you feel comfortable with the material presented here (feel free to explore the references given below!), move on to Kernels, kernel density estimation, kernel regression, which explains which concepts Nuklei implements, and how they are implemented. Keep in mind that the Nuklei implementation doesn't strictly follow the text presented on this page. For instance, in the text below, 3D positions are modeled with Gaussian distributions. Nuklei, on the other hand, offers both Gaussian and triangular kernels for modeling positions, and triangular kernels are used by default in kernel density estimation and kernel logistic regression. | |
| Kernels, kernel density estimation, kernel regression | |
| This page presents the kernel density estimation (KDE) and kernel logistic regression (KLR) tools provided by Nuklei. | |
| Generic Kernels | |
| This page presents a set of generic kernel classes provided by Nuklei. The KDE and KLR-related kernels (e.g., kernel::se3, see Kernels, kernel density estimation, kernel regression) rely on the generic kernels presented here. | |
| Operations on Poses | |
| This page introduces vocabulary for referring to operations on \( SE(3) \) poses. | |
| F.A.Q. | |
| Coding Details | |
| Configuration | |
| The behavior of the functions implemented in Nuklei can be altered through environment variables: | |
This group contains documentation useful for working with Nuklei (as opposed to contributing to Nuklei).