Enhancing Door Detection for Autonomous Mobile Robots with Environment-Specific Data Collection

Door detection represents a fundamental capability for autonomous mobile robots employed in tasks involving indoor navigation. Recognizing the presence of a door and its status (open or closed) can induce a remarkable impact on the navigation performance, especially for dynamic settings where doors can enable or disable passages, hence changing the actual topology of the map. In this work, we address the problem of building a door detector module for an autonomous mobile robot deployed in a long-term scenario, namely operating in the same environment for a long time, thus observing the same set of doors from different points of view. First, we show how the mainstream approach for door detection, based on object recognition, falls short in considering the constrained perception setup typical of a mobile robot. Hence, we devise a method to build a dataset of images taken from a robot's perspective and we exploit it to obtain a door detector based on an established deep-learning object-recognition method. We then exploit the long-term assumption of our scenario to qualify the model on the robot working environment via fine-tuning with additional images acquired in the deployment environment. Our experimental analysis shows how this method can achieve good performance and highlights a trade-off between costs and benefits of the fine-tuning approach.