Dear colleagues,

Over the past few years, deep learning techniques have permitted tackling a number of problems in regard to the recognition of scenes or of classification from visual information and other sensors, with promising results. More recently, their ability to analyze and detect patterns from large amounts of data has broadened their applications, and they can be used to solve a variety of problems in robotics, such as mapping and localization. Deep learning techniques may constitute a powerful alternative, either when the main sensorial system is a vision system that provides enormous amounts of information, or when this visual information is merged with data provided by other types of sensors, such as range sensors.

In this sense, different techniques have recently emerged, either supervised or unsupervised, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks, generative adversarial networks, and siamese networks. These techniques have shown excellent performance in a variety of tasks, usually related to classification. However, the use of deep learning techniques to address the problem of mapping and/or the localization of mobile robots, undoubtedly offers great possibilities for any type of robot (drones, ground vehicles, or submarine robots, etc.). Recognizing, identifying, and modeling scenarios from the information provided by multiple sensory systems is essential in order to carry out tasks related to autonomous navigation in mobile robots. In this field, deep learning techniques provide a novel alternative to describe the environments in which the robot operates.

The aim of this Special Issue is to present different alternatives or applications to resolve the problem of the mapping and/or localization of a mobile robots using deep learning techniques. Additionally, state-of-the-art reviews of a specific problem in this field, and how it has been addressed through the use of deep learning, would also be welcomed.

This Special Issue invites contributions related to—but not limited to—the following topics:

• place recognition and/or image retrieval by means of deep learning techniques;
• metric, topological, or hybrid mapping through deep learning techniques;
• modeling environments through long-short term memories;
• long-term mapping and localization;
• computer vision and deep learning in mapping and localization;
• fusion of information in multi-sensor systems;
• movement estimation using deep learning techniques;
• human–robot interaction in mapping and localization;
• navigation in social and/or crowded environments;
• path following in challenging environments;
• simultaneous localization and mapping (SLAM);
• visual odometry and trajectory estimation;
• loop closure detection;
• deep learning and autonomous driving;

Prof. Dr. Oscar Reinoso Garcia
Prof. Dr. Luis Payá
Guest Editors

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