  This draft summarizes some basics about geometric computer vision needed to
implement efficient computer vision algorithms for applications that use
measurements from at least one digital camera mounted on a moving platform with
a special focus on automotive applications processing image streams taken from
cameras mounted on a car. Our intention is twofold: On the one hand, we would
like to introduce well-known basic geometric relations in a compact way that
can also be found in lecture books about geometric computer vision like [1, 2].
On the other hand, we would like to share some experience about subtleties that
should be taken into account in order to set up quite simple but robust and
fast vision algorithms that are able to run in real time. We added a
conglomeration of literature, we found to be relevant when implementing basic
algorithms like optical flow, visual odometry and structure from motion. The
reader should get some feeling about how the estimates of these algorithms are
interrelated, which parts of the algorithms are critical in terms of robustness
and what kind of additional assumptions can be useful to constrain the solution
space of the underlying usually non-convex optimization problems.
