Abstract
Place recognition is one of the most fundamental topics in the computer-vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of knowledge accumulated in this field, place recognition still remains an open problem due to the various ways in which the appearance of real-world places may differ. This paper presents an overview of the place-recognition literature. Since condition-invariant and viewpoint-invariant features are essential factors to long-term robust visual place-recognition systems, we start with traditional image-description methodology developed in the past, which exploits techniques from the image-retrieval field. Recently, the rapid advances of related fields, such as object detection and image classification, have inspired a new technique to improve visual place-recognition systems, that is, convolutional neural networks (CNNs). Thus, we then introduce the recent progress of visual place-recognition systems based on CNNs to automatically learn better image representations for places. Finally, we close with discussions and mention of future work on place recognition.
Highlights
Place recognition has attracted a significant amount of attention in the computer-vision and robotics communities, as evidenced by the related citations and a number of workshops dedicated to improving long-term robot navigation and autonomy [1]
How can we robustly identify the same real-world place undergoing major changes in appearance (e.g., illumination variation (Figure 1), change of seasons (Figure 2) or weather, structural modifications over time, and viewpoint change)? To be clear, the above changes in appearance are summarized as conditional variations, but exclude viewpoint change
This paper provides an overview of both traditional and deep-learning-based descriptive techniques widely applied to place-recognition tasks, which is by no means exhaustive
Summary
Place recognition has attracted a significant amount of attention in the computer-vision and robotics communities, as evidenced by the related citations and a number of workshops dedicated to improving long-term robot navigation and autonomy [1]. Place recognition was dominated by sophisticated local-invariant feature extractors, such as Scale-Invariant Feature Transformation (SIFT) [3] and Speed-Up Robust Features (SURF) [4], hand-crafted global image descriptors, such as Generalized Search Trees (GIST) [5,6], and the bag-of-visual-words [7,8] approach. These traditional feature-extraction techniques have gained impressive results.
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