Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.
Visual place recognition is the task of localizing a robot in an environment using information from a single camera. It is a well studied problem and can be split into two dominant approaches; feature based, that represent a particular image as a set of key features extracted from interesting location in the image, and image-based, that instead of extracting features, tries to reason using all the image information @cite_20 . Each approach has it own limitations: feature-based methods may fail under illumination changes while image-based methods are sensitive to view-point changes.
@cite_20
2284029970 2130917470 2038034534 2262703527
Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description. An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this we present an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach. This paper describes an online place discovery and recognition engine that fuses information over time to create topologically distinct places. A key motivation is the recognition that a single image may be a poor exemplar of what constitutes a place. Images are not ‘places’ nor are they ‘documents’. Instead, by treating image-sequences as a multimodal distribution over topics – and by discovering topics incrementally and online – it is possible to both reduce the memory footprint of place recognition systems, and to improve precision and recall. Distinctive key-places are represented by a cluster topics found from the covisibility graph of a relative simultaneous localization and mapping engine – key-places inherently span many images. A dynamic vocabulary of visual words and density based clustering is used to continually estimate a set of visual topics, changes in which drive the placerecognition process. The system is evaluated using an indoor robot sequence, a standard outdoor robot sequence and a longterm sequence from a static camera. Experiments demonstrate qualitatively distinct themes associated with discovered places – from common place types such as ‘hallway’, or ‘desk-area’, to temporal concepts such as ‘dusk’, ‘dawn’ or ‘mid-day’. Compared to traditional image-based place-recognition, this reduces the information that must be stored without reducing place-recognition performance. Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of recognizing a revisited location increases with appearance changes caused, for instance, by weather or illumination variations, which hinders the long-term application of such algorithms in real environments. In this paper we present a convolutional neural network (CNN), trained for the first time with the purpose of recognizing revisited locations under severe appearance changes, which maps images to a low dimensional space where Euclidean distances represent place dissimilarity. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. The triplets are selected in such way that two samples are from the same location and the third one is taken from a different place. We validate our system through extensive experimentation, where we demonstrate better performance than state-of-art algorithms in a number of popular datasets.