Door-status detection, namely recognizing the presence of a door and its status (open or closed), can induce a remarkable impact on a mobile robot's navigation performance, especially for dynamic settings where doors can enable or disable passages, changing the topology of the map. In this work, we address the problem of building a door-status detector module for a mobile robot 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 to improve the mainstream approach based on object detection by 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-status detector based on deep learning. We then leverage the typical working conditions of a robot to qualify the model for boosting its performance in the working environment via fine-tuning with additional data. Our experimental analysis shows the effectiveness of this method with results obtained both in simulation and in the real-world, that also highlight a trade-off between costs and benefits of the fine-tuning approach.
翻译:门状态检测,即承认门的存在及其状态(开放或封闭),可以对移动机器人的导航性能产生显著影响,特别是对于动态环境,门可以启用或禁用通道,改变地图的地形。在这项工作中,我们处理为在同一环境中长期运行的移动机器人建造门状态检测模块的问题,从而从不同角度观察同样的一组门。首先,我们考虑到移动机器人典型的受限感知设置,表明如何改进基于物体检测的主流方法。因此,我们设计了一种方法,从机器人的角度建立图像数据集,利用它获得一个基于深层学习的门状态检测器。然后我们利用机器人的典型工作条件,通过微调补充数据,来使其在工作环境中的性能得到提升。我们的实验分析表明这种方法的有效性,在模拟和现实世界中都取得了结果,这也突显了微调方法的成本和效益之间的权衡。