[Paper Review] Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

Arman Asgharpoor Golroudbari, Mohammad Hossein Sabour|arXiv (Cornell University)|Feb 22, 2023

Robotic Path Planning Algorithms4 citations

TL;DR

This comprehensive review synthesizes recent advancements in deep learning for autonomous navigation, focusing on end-to-end frameworks integrating perception, localization, mapping, path planning, and control. It evaluates CNNs, RNNs, transformers, and reinforcement learning in object detection, semantic segmentation, SLAM, and decision-making, highlighting improved accuracy and robustness while identifying challenges in interpretability, safety, and real-world generalization.

ABSTRACT

This review article is an attempt to survey all recent AI based techniques used to deal with major functions in This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

Motivation & Objective

• To provide a comprehensive overview of deep learning applications in autonomous navigation across perception, localization, mapping, and control.
• To analyze the integration of deep learning with traditional navigation components such as SLAM, obstacle avoidance, and motion planning.
• To evaluate the performance, scalability, and limitations of end-to-end deep learning frameworks in dynamic and uncertain environments.
• To identify open challenges in robustness, interpretability, and safety for real-world deployment of deep learning-based navigation systems.
• To guide researchers and practitioners by correlating current methods with future research directions in autonomous navigation.

Proposed method

• Systematic review of peer-reviewed literature (2015–2023) focusing on deep learning applications in autonomous navigation.
• Categorization of deep learning techniques by function: convolutional neural networks (CNNs) for perception and segmentation, RNNs/LSTMs for sequential modeling, and transformers for attention-based feature learning.
• Analysis of end-to-end frameworks combining perception, localization, and control using joint training of perception and decision-making modules.
• Evaluation of sensor fusion techniques combining data from LiDAR, cameras, IMUs, and radar using deep learning-based fusion networks.
• Examination of reinforcement learning (DRL) methods such as DQN and PPO for policy learning in navigation tasks.
• Incorporation of attention mechanisms and explainability tools to improve model interpretability and trust in autonomous systems.

Figure 1: Structure of an artificial neuron

Experimental results

Research questions

• RQ1 How have deep learning models improved object detection and semantic segmentation in autonomous navigation systems?
• RQ2 What are the key advantages and limitations of end-to-end deep learning frameworks in autonomous navigation compared to modular approaches?
• RQ3 How do deep learning-enhanced SLAM systems outperform traditional methods in dynamic or complex environments?
• RQ4 To what extent can deep reinforcement learning learn robust navigation policies in real-world, uncertain environments?
• RQ5 What are the major challenges in deploying deep learning models for autonomous navigation in terms of safety, interpretability, and robustness?

Key findings

• Convolutional Neural Networks (CNNs) such as Faster R-CNN, SSD, and YOLO have significantly improved real-time object detection accuracy and inference speed in autonomous navigation.
• U-Net and DeepLab architectures have achieved state-of-the-art performance in semantic segmentation, enabling pixel-level scene understanding crucial for safe navigation.
• Deep learning-based SLAM systems like Deep SLAM and ORB-SLAM with learned features outperform traditional methods in feature extraction and matching under challenging conditions such as low texture or dynamic objects.
• Reinforcement learning algorithms like DQN and PPO demonstrate strong potential in learning complex navigation policies through trial-and-error, especially in environments with partial observability.
• Despite high performance, deep learning models remain vulnerable to adversarial conditions such as poor lighting, occlusions, and sensor noise, highlighting a key limitation for real-world deployment.
• Interpretability remains a major challenge; attention mechanisms and visualization tools are emerging as essential for understanding and validating model decisions in safety-critical applications.

Figure 2: Structure of a deep feedforward neural network