Deep Learning Perspective of Scene Understanding in Autonomous Robots
It provides a comprehensive overview for researchers and practitioners in robotics and AI, but is incremental as it synthesizes existing work rather than introducing new methods.
This paper reviews deep learning applications for scene understanding in autonomous robots, covering object detection, segmentation, depth estimation, and SLAM to address limitations of traditional geometric models and improve real-time performance in unstructured environments.
This paper provides a review of deep learning applications in scene understanding in autonomous robots, including innovations in object detection, semantic and instance segmentation, depth estimation, 3D reconstruction, and visual SLAM. It emphasizes how these techniques address limitations of traditional geometric models, improve depth perception in real time despite occlusions and textureless surfaces, and enhance semantic reasoning to understand the environment better. When these perception modules are integrated into dynamic and unstructured environments, they become more effective in decisionmaking, navigation and interaction. Lastly, the review outlines the existing problems and research directions to advance learning-based scene understanding of autonomous robots.