  In many domains that involve the use of sensors, such as robotics or sensor
networks, there are opportunities to use some form of active sensing to
disambiguate data from noisy or unreliable sensors. These disambiguating
actions typically take time and expend energy. One way to choose the next
disambiguating action is to select the action with the greatest expected
entropy reduction, or information gain. In this work, we consider active
sensing in aid of stereo vision for robotics. Stereo vision is a powerful
sensing technique for mobile robots, but it can fail in scenes that lack strong
texture. In such cases, a structured light source, such as vertical laser line
can be used for disambiguation. By treating the stereo matching problem as a
specially structured HMM-like graphical model, we demonstrate that for a scan
line with n columns and maximum stereo disparity d, the entropy minimizing aim
point for the laser can be selected in O(nd) time - cost no greater than the
stereo algorithm itself. In contrast, a typical HMM formulation would suggest
at least O(nd^2) time for the entropy calculation alone.
