  We investigate methods for determining if a planar surface contains geometric
deviations (e.g., protrusions, objects, divots, or cliffs) using only an
instantaneous measurement from a miniature optical time-of-flight sensor. The
key to our method is to utilize the entirety of information encoded in raw
time-of-flight data captured by off-the-shelf distance sensors. We provide an
analysis of the problem in which we identify the key ambiguity between geometry
and surface photometrics. To overcome this challenging ambiguity, we fit a
Gaussian mixture model to a small dataset of planar surface measurements. This
model implicitly captures the expected geometry and distribution of
photometrics of the planar surface and is used to identify measurements that
are likely to contain deviations. We characterize our method on a variety of
surfaces and planar deviations across a range of scenarios. We find that our
method utilizing raw time-of-flight data outperforms baselines which use only
derived distance estimates. We build an example application in which our method
enables mobile robot obstacle and cliff avoidance over a wide field-of-view.
