Rule-Based Door Detection Using Laser Range Data in Indoor Environments

In indoor environments, doors generally separate different parts of buildings such as rooms and corridors. The information whether a robot is at a door location is valuable for navigation, localization, mapping, and exploration tasks. Existing algorithms focus on vision-based methods which are supported by laser range data to detect closed doors along a corridor. In this paper, a rule-based door detection method that uses only laser range data is presented. The proposed method is based on three assumptions: Firstly, the doors must be open. Also, the robot should be near and/or between the door frames for proper detection of the door. Lastly, doors are assumed to be along the axes of the reference coordinate frame. Under these assumptions, there is always a bottleneck at a certain location in the shape formed by the laser beam readings. Our method exploits this observation to define a set of rules for detecting doors. Simulations are performed using Freiburg 79 and ESOGU Electrical Engineering Laboratory buildings data to measure the performance of the proposed algorithm. The algorithm detected 90% of the doors for both environments with a false positive rate smaller than 6.1% and 0.7% for the Freiburg 79 and ESOGU buildings, respectively.