Improving Door Detection for Mobile Robots by fusing Camera and Laser-Based Sensor Data Jens Hensler, Michael Blaich, and Oliver Bittel University of Applied Sciences Brauneggerstr. 55, 78462 Konstanz, Germany {jhensler, mblaich, bittel}@htwg-konstanz.de Abstract. For many indoor robot applications it is crucial to recognize doors reliably. Doors connect rooms and hallways and therefore deter- mine possible navigation paths. Moreover, doors are important land- marks for robot self-localization and map building. Existing algorithms for door detection are often limited to restricted environments. They do not consider the large intra-class variability of doors. In this paper we present a set of simple door detection classifiers, which are based either on camera or laser-based data. Separately, these classifier accomplish only a weak door detection rate. However, by combining them through a AdaBoost Algorithm more than 82% of all doors with a false positive rate less than 3% are detected in static test data. Further improvement can easily achieved by using different door perspectives from a moving robot. In a realtime mobile robot application we detect more than 90% of all doors with a very low false detection rate. Key words: Door Detection, AdaBoost, Learning Algorithm, Mobile Robot 1 Introduction In an indoor environment doors constitute significant landmarks. They represent the entrance and exit points of rooms. Therefore, robust real-time door detection is an essential component for indoor robot applications (e.g. courier, observation or tour guide robots). In the past, the problem of door detection has been studied several times. The approaches differ in the implemented sensor systems and the diversity of environments and doors, respectively. For example, in [Murillo et al., 2008] and [Chen and Birchfield, 2008] only visual information was used. Others, like [Anguelov et al., 2004] apply an additional 2D laser range finder and thereby receive better results. From these approaches we find that there are two major difficulties in au- tonomous door detection. Firstly, it is often impossible to cover the entire door in a single camera image. In our scenario, the robot camera is close to the ground so that the top of the door is often not captured by the robot’s camera (see figure 1).