gradslam is a fully differentiable dense SLAM framework. It provides a repository of differentiable building blocks
for a dense SLAM system, such as differentiable nonlinear least squares solvers, differentiable ICP
(iterative closest point) techniques, differentiable raycasting modules, and differentiable mapping/fusion blocks.
One can use these blocks to construct SLAM systems that allow gradients to flow all the way from the outputs of the
system (map, trajectory) to the inputs (raw color/depth images, parameters, calibration, etc.).
