  Borrowing terminology from fluid mechanics, the concepts of {\em Eulerian}
and {\em Lagrangian optical flow sensing} are introduced. Eulerian optical flow
sensing assumes that each photoreceptor in the camera or eye can
instantaneously detect feature image points and their velocities on the retina.
If this assumption is satisfied, even a two pixel imaging system can provide a
moving agent with information about its movement along a corridor that is
sufficiently precise as to be used as a robustly reliable steering signal.
Implementing Eulerian optical flow sensing poses significant challenges,
however. Lagrangian optical flow, on the other hand, tracks feature image
points as they move on the retina. This form of visual sensing is the basis for
many standard computer vision implementations, including Lukas-Kanade and
Horn-Schunck. Lagrangian optical flow has its own challenges, not least of
which is that it is badly confounded by rotational components of motion.
Combined steering and sensing strategies for mitigating the effects of
rotational motions are considered.
