  Stereo cameras are a popular choice for obstacle avoidance for outdoor
lighweight, low-cost robotics applications. However, they are unable to sense
thin and reflective objects well. Currently, many algorithms are tuned to
perform well on indoor scenes like the Middlebury dataset. When navigating
outdoors, reflective objects, like windows and glass, and thin obstacles, like
wires, are not well handled by most stereo disparity algorithms. Reflections,
repeating patterns and objects parallel to the cameras' baseline causes
mismatches between image pairs which leads to bad disparity estimates. Thin
obstacles are difficult for many sliding window based disparity methods to
detect because they do not take up large portions of the pixels in the sliding
window. We use a trinocular camera setup and micropolarizer camera capable of
detecting reflective objects to overcome these issues. We present a
hierarchical disparity algorithm that reduces noise, separately identify wires
using semantic object triangulation in three images, and use information about
the polarization of light to estimate the disparity of reflective objects. We
evaluate our approach on outdoor data that we collected. Our method contained
an average of 9.27% of bad pixels compared to a typical stereo algorithm's
18.4% of bad pixels in scenes containing reflective objects. Our trinocular and
semantic wire disparity methods detected 53% of wire pixels, whereas a typical
two camera stereo algorithm detected 5%.
