A Comprehensive Introduction of Visual-Inertial Navigation

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Control and visual navigation for unmanned underwater vehicles

Ph. D. Thesis.Control and navigation systems are key for any autonomous robot. Due to environmental disturbances, model uncertainties and nonlinear dynamic systems, reliable functional control is essential and improvements in the controller design can significantly benefit the overall performance of Unmanned Underwater Vehicles (UUVs). Analogously, due to electromagnetic attenuation in underwater environments, the navigation of UUVs is always a challenging problem. In this thesis, control and navigation systems for UUVs are investigated. In the control field, four different control strategies have been considered: Proportional-Integral-Derivative Control (PID), Improved Sliding Mode Control (SMC), Backstepping Control (BC) and customised Fuzzy Logic Control (FLC). The performances of these four controllers were initially simulated and subsequently evaluated by practical experiments in different conditions using an underwater vehicle in a tank. The results show that the improved SMC is more robust than the others with small settling time, overshoot, and error. In the navigation field, three underwater visual navigation systems have been developed in the thesis: ArUco Underwater Navigation systems, a novel Integrated Visual Odometry with Monocular camera (IVO-M), and a novel Integrated Visual Odometry with Stereo camera (IVO-S). Compared with conventional underwater navigation, these methods are relatively low-cost solutions and unlike other visual or inertial-visual navigation methods, they are able to work well in an underwater sparse-feature environment. The results show the following: the ArUco underwater navigation system does not suffer from cumulative error, but some segments in the estimated trajectory are not consistent; IVO-M suffers from cumulative error (error ratio is about 3 - 4%) and is limited by the assumption that the “seabed is locally flat”; IVO-S suffers from small cumulative errors (error ratio is less than 2%). Overall, this thesis contributes to the control and navigation systems of UUVs, presenting the comparison between controllers, the improved SMC, and low-cost underwater visual navigation methods

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system

An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry

This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index $n$ and radial pixel coordinate) and a virtual pinhole model (as a function of $n$). We derive the self-calibration formulation leveraging epipolar constraints to estimate the refractive index and subsequently correct for distortion. Through experimental studies using an underwater robot integrating cameras and inertial sensing, the model is validated regarding the accurate estimation of the refractive index and its benefits for robust odometry estimation in an extended envelope of conditions. Lastly, we show the transition between media and the estimation of the varying refractive index online, thus allowing computer vision tasks across refractive media.Comment: 7 pages, 6 figures, Submitted to the IEEE International Conference on Robotics and Automation, 202