  Blending representation learning approaches with simultaneous localization
and mapping (SLAM) systems is an open question, because of their highly modular
and complex nature. Functionally, SLAM is an operation that transforms raw
sensor inputs into a distribution over the state(s) of the robot and the
environment. If this transformation (SLAM) were expressible as a differentiable
function, we could leverage task-based error signals to learn representations
that optimize task performance. However, several components of a typical dense
SLAM system are non-differentiable. In this work, we propose gradSLAM, a
methodology for posing SLAM systems as differentiable computational graphs,
which unifies gradient-based learning and SLAM. We propose differentiable
trust-region optimizers, surface measurement and fusion schemes, and
raycasting, without sacrificing accuracy. This amalgamation of dense SLAM with
computational graphs enables us to backprop all the way from 3D maps to 2D
pixels, opening up new possibilities in gradient-based learning for SLAM.
  TL;DR: We leverage the power of automatic differentiation frameworks to make
dense SLAM differentiable.
