🤖 AI Summary
Traditional geometric models face fundamental limitations in real-time depth perception, occlusion handling, and modeling of textureless surfaces. To address these challenges, this paper presents a systematic review and advancement of deep learning–driven scene understanding for autonomous robots. We propose an end-to-end framework integrating CNN-Transformer hybrid architectures, self-supervised depth estimation, multi-task joint training, and NeRF-enhanced representation learning. The framework significantly improves the synergistic performance of semantic segmentation, 3D reconstruction, and visual SLAM in dynamic, unstructured environments. Key innovations include occlusion-robust dense depth inference and cross-modal semantic-geometric joint representation, which collectively enhance robots’ real-time perception, navigation decision-making, and physical interaction capabilities. This work establishes a unified methodology and scalable technical pathway for learning-based embodied scene understanding.
📝 Abstract
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.