# Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

arXiv: https://arxiv.org/abs/2001.02319 · HTML: https://mlsanity.com/paper/2001.02319/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 30 of 30)

- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Unsupervised Monocular Depth Estimation in Highly Complex Environments](https://mlsanity.com/paper/2107.13137/) (2021)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Vision-Language Embodiment for Monocular Depth Estimation](https://mlsanity.com/paper/2503.16535/) (2025)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes](https://mlsanity.com/paper/2309.16019/) (2023)
- [Research on Autonomous Robots Navigation based on Reinforcement Learning](https://mlsanity.com/paper/2407.02539/) (2024)
- [MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation](https://mlsanity.com/paper/2303.01396/) (2023)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Embodiment: Self-Supervised Depth Estimation Based on Camera Models](https://mlsanity.com/paper/2408.01565/) (2024)
- [Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Prediction](https://mlsanity.com/paper/2507.23021/) (2025)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Any to Full: Prompting Depth Anything for Depth Completion in One Stage](https://mlsanity.com/paper/2603.05711/) (2026)
- [UniCT Depth: Event-Image Fusion Based Monocular Depth Estimation with Convolution-Compensated ViT Dual SA Block](https://mlsanity.com/paper/2507.19948/) (2025)
- [Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks](https://mlsanity.com/paper/2410.16303/) (2024)
- [Visual Heading Prediction for Autonomous Aerial Vehicles](https://mlsanity.com/paper/2512.09898/) (2025)
- [Computer vision tasks for intelligent aerospace missions: An overview](https://mlsanity.com/paper/2407.06513/) (2024)
- [ECLipsE-Gen-Local: Efficient Compositional Local Lipschitz Estimates for Deep Neural Networks](https://mlsanity.com/paper/2510.05261/) (2025)
- [Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning](https://mlsanity.com/paper/2211.07147/) (2022)
- [Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems](https://mlsanity.com/paper/2403.16132/) (2024)
- [Efficient model predictive control for nonlinear systems modelled by deep neural networks](https://mlsanity.com/paper/2405.10372/) (2024)
- [Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles](https://mlsanity.com/paper/2504.01336/) (2025)
- [DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting](https://mlsanity.com/paper/2504.01531/) (2025)
- [A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions](https://mlsanity.com/paper/2504.11995/) (2025)
- [Federated Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2505.23292/) (2025)
- [DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures](https://mlsanity.com/paper/2511.15503/) (2025)
- [JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2607.26600/) (2026)

## Abstract (arXiv)

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.