## 1 Introduction  
Autonomous robots operating over long periods of time, such as days, weeks, or months, face a variety of environmental changes over time. As the environment changes, robots should recognize places using their visual sensors, which is called long-term visual place recognition. It is an essential component for achieving long-term simultaneous localization and mapping (SLAM) and autonomous navigation [1]. One of the major problems in long-term visual place recognition is the appearance change problem caused by factors such as time of day or weather conditions [2].  
To solve the appearance change problem in visual place recognition, global descriptors that can describe the whole-image have widely used [3, 4]. Compared to local features such as SIFT [5] and SURF [6], global descriptors are not only robust to illumination changes, but also require less computation since they do not require a keypoint detection phase [1]. The classic hand-crafted global descriptors such as HOG [7] or gist [8, 9] showed higher place recognition performance than the existing local descriptors in a changing environment [3, 4]. However, hand-crafted descriptors have inherent limitations in generalization performance since features are extracted according to predefined parameters.  
Recently, features from deep learning structures have proven to have superior generalization performances than existing hand-crafted methods. In particular, a deep convolutional neural network (CNN), a kind of neural network, is a structure that have shown excellent performance in image recognition and classification [10]. A variety of structures using CNNs have been widely used in visual place recognition [11, 12, 13, 14]. A sequence of image features using CNNs was used to find the same places between different seasons in [15]. [PERSON] et al. evaluated CNNs features from each layer of pretrained AlexNet [10] for visual place recognition in a changing environment. Another deep learning structure, the autoencoder, has been also used for visual place recognition because the output of each layer can be used as an image descriptor. [PERSON] and [PERSON] [16] used a deep convolutional autoencoder (CAE) for feature extraction, and [PERSON] [17] proposed an illumination-compensated CAE for robust place recognition.  
In this paper, we propose a novel feature extraction method based on variational autoencoders (VAEs) [18]. It is one of the popular models for unsupervised representation learning, and showed outstanding performance in feature learning [19; 20]. It consists of a standard autoencoder component, and can approximate Bayesian inference for latent variable models. To obtain robust performances in a changing environment, we assume that the image \(\mathbf{x}\) is generated from the latent variable \(\mathbf{z}\), and this latent representation is divided into the condition-invariant feature \(\mathbf{z}_{p}\) and the condition-sensitive feature \(\mathbf{z}_{c}\). To find the same places from different conditions, comparing the condition-invariant features improves the performance of place recognition. The proposed procedure is shown in Figure 1.  
Our paper is organized as follows. Section 2 explains the basic preliminaries of VAEs. The proposed structures for feature extraction using the context information is explained in Section 3. Then, the robot localization using the extracted condition-invariant feature is discussed in Section 4. Section 5 presents the validation of the proposed method through publicly available datasets with other algorithms. Finally, Section 6 concludes the paper.