Ghasemieh Alireza, Kashef Rasha
Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel). 2024 Dec 17;24(24):8040. doi: 10.3390/s24248040.
Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera's pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry's technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications.
自主技术已经彻底改变了交通运输、军事行动和太空探索，这使得在传统基于全球定位系统（GPS）的系统不可靠或无法使用的环境中进行精确定位成为必要。虽然GPS系统在户外定位中广泛应用，但在诸如密集城市地区、森林和室内空间等有遮挡的环境中，GPS系统面临着局限性。此外，对GPS的依赖会使系统容易受到信号干扰的影响，这可能导致重大的操作故障。因此，开发不依赖外部信号的替代定位技术至关重要，这表明迫切需要适用于不同应用的强大的、独立于GPS的定位解决方案，从地球上的自动驾驶车辆到火星上的机器人任务。本文通过使用视觉里程计（VO）来解决这些挑战，视觉里程计通过分析为自动驾驶车辆（AV）量身定制的GPS信号被阻断区域中捕获的图像序列来估计相机的姿态，在这些区域中，安全性和实时决策至关重要。已经有大量研究致力于使用激光雷达或立体相机进行姿态估计，尽管它们精度很高，但受到重量、成本和复杂性的限制。相比之下，单目视觉既实用又经济高效，使其成为无人机、汽车和自动驾驶车辆的热门选择。然而，强大且可靠的单目姿态估计模型仍未得到充分探索。本研究旨在通过开发一种新颖的自适应框架来填补这一空白，该框架使用配备单目相机的增强型视觉里程计系统进行户外姿态估计和安全导航，特别是针对由于成本或物理限制而无法部署额外传感器的应用。该框架设计为可跨不同车辆和平台进行适配，确保准确可靠的姿态估计。我们整合先进的控制理论为运动控制提供安全保障，确保自动驾驶车辆能够对附近交通代理的紧迫危险和未知轨迹做出安全反应。重点是创建一个符合多传感器系统性能标准的人工智能驱动模型，同时利用单目视觉的固有优势。本研究使用最先进的机器学习技术来提升视觉里程计的技术能力，并确保其在不同平台、相机和环境中的适应性。通过将前沿的视觉里程计技术与强大的控制理论相结合，我们的方法提高了自动驾驶车辆在复杂交通情况下的安全性和性能，直接应对了安全和自适应导航的挑战。在KITTI里程计数据集上的实验结果表明姿态估计精度有显著提高，为实际应用提供了一种经济高效且强大的解决方案。
