  We introduce an approach for the real-time (2Hz) creation of a dense map and
alignment of a moving robotic agent within that map by rendering using a
Graphics Processing Unit (GPU). This is done by recasting the scan alignment
part of the dense mapping process as a rendering task. Alignment errors are
computed from rendering the scene, comparing with range data from the sensors,
and minimized by an optimizer. The proposed approach takes advantage of the
advances in rendering techniques for computer graphics and GPU hardware to
accelerate the algorithm. Moreover, it allows one to exploit information not
used in classic dense mapping algorithms such as Iterative Closest Point (ICP)
by rendering interfaces between the free space, occupied space and the unknown.
The proposed approach leverages directly the rendering capabilities of the GPU,
in contrast to other GPU-based approaches that deploy the GPU as a general
purpose parallel computation platform.
  We argue that the proposed concept is a general consequence of treating
perception problems as inverse problems of rendering. Many perception problems
can be recast into a form where much of the computation is replaced by render
operations. This is not only efficient since rendering is fast, but also
simpler to implement and will naturally benefit from future advancements in GPU
speed and rendering techniques. Furthermore, this general concept can go beyond
addressing perception problems and can be used for other problem domains such
as path planning.
