Deep Learning Perspective of Scene Understanding in Autonomous Robots

TL;DR

This review discusses how deep learning enhances scene understanding in autonomous robots by improving perception, segmentation, and navigation, addressing traditional limitations and enabling better decision-making in complex environments.

Contribution

It provides a comprehensive overview of recent deep learning techniques for scene understanding in autonomous robots and highlights future research directions.

Findings

Deep learning improves object detection and segmentation accuracy.

Enhanced depth estimation and 3D reconstruction in real-time.

Better semantic reasoning for autonomous navigation.

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.