Lab Meeting September 1, 2008 (Casey): Prior Data and Kernel Conditional Random Fields for Obstacle Detection

Authors: Carlos Vallespi, Anthony (Tony) Stentz
Robotics: Science and Systems 2008

Abstract—We consider the task of training an obstacle detection(OD) system based on a monocular color camera usingminimal supervision. We train it to match the performance of asystem that uses a laser rangefinder to estimate the presenceof obstacles by size and shape. However, the lack of rangedata in the image cannot be compensated by the extraction oflocal features alone. Thus, we investigate contextual techniquesbased on Conditional Random Fields (CRFs) that can exploitthe global context of the image, and we compare them to aconventional learning approach. Furthermore, we describe aprocedure for introducing prior data in the OD system to increaseits performance in “familiar” terrains. Finally, we performexperiments using sequences of images taken from a vehicle for autonomous vehicle navigation applications.

[Paper Link]