Invariant-based Mapping of Space During General Motion of an Observer
Juan D. Yepes Daniel Raviv
This paper explores visual motion-based invariants, resulting in a new
instantaneous domain where: a) the stationary environment is perceived as
unchanged, even as the 2D images undergo continuous changes due to camera
motion, b) obstacles can be detected and potentially avoided in specific
subspaces, and c) moving objects can potentially be detected. To achieve this,
we make use of nonlinear functions derived from measurable optical flow, which
are linked to geometric 3D invariants.
  We present simulations involving a camera that translates and rotates
relative to a 3D object, capturing snapshots of the camera projected images. We
show that the object appears unchanged in the new domain over time. We process
real data from the KITTI dataset and demonstrate how to segment space to
identify free navigational regions and detect obstacles within a predetermined
subspace. Additionally, we present preliminary results, based on the KITTI
dataset, on the identification and segmentation of moving objects, as well as
the visualization of shape constancy.
  This representation is straightforward, relying on functions for the simple
de-rotation of optical flow. This representation only requires a single camera,
it is pixel-based, making it suitable for parallel processing, and it
eliminates the necessity for 3D reconstruction techniques.