Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

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.

End-to-End Deep Learning Framework for Real-Time Inertial Attitude Estimation using 6DoF IMU

Inertial Measurement Units (IMU) are commonly used in inertial attitude estimation from engineering to medical sciences. There may be disturbances and high dynamics in the environment of these applications. Also, their motion characteristics and patterns also may differ. Many conventional filters have been proposed to tackle the inertial attitude estimation problem based on IMU measurements. There is no generalization over motion and environmental characteristics in these filters. As a result, the presented conventional filters will face various motion characteristics and patterns, which will limit filter performance and need to optimize the filter parameters for each situation. In this paper, two end-to-end deep-learning models are proposed to solve the problem of real-time attitude estimation by using inertial sensor measurements, which are generalized to motion patterns, sampling rates, and environmental disturbances. The proposed models incorporate accelerometer and gyroscope readings as inputs, which are collected from a combination of seven public datasets. The models consist of convolutional neural network (CNN) layers combined with Bi-Directional Long-Short Term Memory (LSTM) followed by a Fully Forward Neural Network (FFNN) to estimate the quaternion. To evaluate the validity and reliability, we have performed an extensive and comprehensive evaluation over seven publicly available datasets, which consist of more than 120 hours and 200 kilometers of IMU measurements. The results show that the proposed method outperforms the state-of-the-art methods in terms of accuracy and robustness. Furthermore, it demonstrates that this model generalizes better than other methods over various motion characteristics and sensor sampling rates.