Efficient Sensing, Learning and Vision for Autonomous Robotics

Dear Colleagues,

Autonomous robots are becoming increasingly more intelligent, powerful, and practical in real-world applications, due to the great advancements in machine learning, computer vision, and artificial intelligence. In order to correctly perceive and understand the 3D world in space and time and be able to act intelligently and fast, efficient robotics systems should be equipped with accurate sensors of small dimensions, learn unsupervised from large quantities of data, and compute fast and at low cost the most advanced vision, navigation, and planning algorithms. This Special Issue aims to bring together state-of-the-art research in vision, sensing, and learning for autonomous robots and UAVs, in order to find the right balance and synergy between the research topics involved and thus strengthen the next steps required in the development of future intelligent machines.

Topics of interest include but are not limited to the following:

1. Computer vision for autonomous robots, self-driving cars, and UAVs;
2. Efficient deep learning techniques for autonomous robots, self-driving cars, and UAVs;
3. Semantic segmentation and interpretation of the visual scene from video and spatiotemporal data;
4. Unsupervised and semi-supervised learning from unlabeled videos and spatiotemporal data;
5. Efficient visual navigation and mapping for robotics;
6. Predicting trajectories and obstacle avoidance for self-driving cars, UAVs, and autonomous robots;
7. 3D modeling, scene perception, and reconstruction for robotics;
8. Efficient real-time object detection for self-driving cars, robotics, and UAVs;
9. Multi-task learning for robotics;
10. Efficient computer vision and learning for embedded systems;
11. Virtual and augmented reality for robotics.

Dr. Marius Leordeanu
Guest Editor

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