Visual door detection integrating appearance and shape cues

An important component of human–robot interaction is the capability to associate semantic concepts with encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Differing from previous approaches, the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environment. It shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.