深度学习让机器人更懂环境，实时感知更准更智能。
Deep Learning Perspective of Scene Understanding in Autonomous Robots
- 用深度学习替代传统几何模型，提升环境理解能力
- 实现实时深度感知，应对遮挡和无纹理表面挑战
- 适合自动驾驶、机器人导航等动态场景应用
本文综述了深度学习在自主机器人场景理解中的应用，涵盖目标检测、语义与实例分割、深度估计、3D重建及视觉SLAM等技术。强调这些方法如何克服传统几何模型的局限，实现在遮挡和无纹理表面上的实时深度感知，并增强语义推理能力以更好理解环境。当这些感知模块集成于动态非结构化环境时，显著提升了决策、导航与交互效果。最后，本文梳理了当前存在的问题与未来研究方向，推动基于学习的机器人场景理解发展。
原文摘要 · Abstract (English)
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.
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