User Configurable Localization

Despite recent advancements in AV localization, it is still arguable that versatile navigation has been achieved. In view of this matter, there is a need to develop a unified framework for localization that can handle various cases both for mobile robotics and AV.
So, what we have now is a robust Particle Filter engine with multiple selectable and/or combinable observation model. Please note that observation model is tightly related to map representation. The basic idea is each sensor input must be projected into the map so that a particle which is being evaluated can be weighted based on some assessment value it gets from the map. They are listed below:

Methods

Global Nearest Neighbor DA (not related to PF)

The component of the matrix is defined \(D_{ij}^{ }=\left(d_{ij}\right)^2\) 

Particle Filter Engine

Model the problem of localization by treating it as a dynamic system:
  1. It evolves by input and noise, in this case, a robot is moving through space: 
  2. Observability equation, that is modeling what sensor sees: \(\mathbf{z}_k=h_k(\mathbf{x}_k,\mathbf{u}_k,\mathbf{n}_k)\)