IEEE Trans Cybern. 2022 Aug;52(8):7265-7276. doi: 10.1109/TCYB.2021.3052499. Epub 2022 Jul 19.
Indoor place category recognition for a cleaning robot is a problem in which a cleaning robot predicts the category of the indoor place using images captured by it. This is similar to scene recognition in computer vision as well as semantic mapping in robotics. Compared with scene recognition, the indoor place category recognition considered in this article differs as follows: 1) the indoor places include typical home objects; 2) a sequence of images instead of an isolated image is provided because the images are captured successively by a cleaning robot; and 3) the camera of the cleaning robot has a different view compared with those of cameras typically used by human beings. Compared with semantic mapping, indoor place category recognition can be considered as a component in semantic SLAM. In this article, a new method based on the combination of a probabilistic approach and deep learning is proposed to address indoor place category recognition for a cleaning robot. Concerning the probabilistic approach, a new place-object fusion method is proposed based on Bayesian inference. For deep learning, the proposed place-object fusion method is trained using a convolutional neural network in an end-to-end framework. Furthermore, a new recurrent neural network, called the Bayesian filtering network (BFN), is proposed to conduct time-domain fusion. Finally, the proposed method is applied to a benchmark dataset and a new dataset developed in this article, and its validity is demonstrated experimentally.
室内场所类别识别是指清洁机器人利用自身拍摄的图像来预测室内场所的类别。这类似于计算机视觉中的场景识别以及机器人学中的语义映射。与场景识别相比，本文所考虑的室内场所类别识别具有以下不同之处：1）室内场所包含典型的家居物体；2）由于图像是由清洁机器人连续拍摄的，因此提供的是一系列图像而不是孤立的图像；3）清洁机器人的摄像头与人类通常使用的摄像头的视角不同。与语义映射相比，室内场所类别识别可以被视为语义 SLAM 的一个组成部分。在本文中，提出了一种基于概率方法和深度学习相结合的新方法来解决清洁机器人的室内场所类别识别问题。关于概率方法，提出了一种基于贝叶斯推断的新场所-物体融合方法。对于深度学习，使用卷积神经网络在端到端框架中对所提出的场所-物体融合方法进行训练。此外，提出了一种新的递归神经网络，称为贝叶斯滤波网络（BFN），用于进行时域融合。最后，将所提出的方法应用于基准数据集和本文开发的新数据集，并通过实验验证了其有效性。
