Karimi M M, Mosavi M R
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
Sci Rep. 2024 Dec 28;14(1):30937. doi: 10.1038/s41598-024-81808-x.
In today's technologically advanced landscape, precision in navigation and positioning holds paramount importance across various applications, from robotics to autonomous vehicles. A common predicament in location-based systems is the reliance on Global Positioning System (GPS) signals, which may exhibit diminished accuracy and reliability under certain conditions. Moreover, when integrated with the Inertial Navigation System (INS), the GPS/INS system could not provide a long-term solution for outage problems due to its accumulated errors. This article introduces a novel graph-based method that utilizes a dynamically adjustable fuzzy window to improve navigation and positioning accuracy. This approach effectively integrates GPS data with Visual-Inertial Odometry (VIO). Additionally, it proposes a novel technique for motion estimation and feature extraction, called Adaptive Feature-Flow Fusion, which facilitates robust performance in environments with both high- and low-feature content. The proposed GPS/VIO system is a compelling solution for mitigating GPS-based navigation's accuracy and reliability concerns. It was implemented and tested on Jetson's embedded board platform to ensure optimal and real-time system performance. The results from these tests are comprehensively detailed in this article. The system underwent stringent evaluation using the KITTI dataset, demonstrating significant accuracy improvements compared to the Extended Kalman Filter (EKF) system. Specifically, the GPS/VIO system exhibited remarkable accuracy improvements of 84.59% and 88.806% during GPS outages lasting 10 and 27 s, respectively. Furthermore, to enhance precision, data preprocessing techniques were incorporated. These techniques involve optimizing image data by adjusting contrast and brightness levels and applying noise reduction to the Inertial Measurement Unit (IMU) data. This resulted in a substantial 44.7% accuracy enhancement for predefined trajectories.
在当今技术先进的环境中，导航和定位的精度在从机器人技术到自动驾驶车辆等各种应用中都至关重要。基于位置的系统中一个常见的困境是对全球定位系统（GPS）信号的依赖，在某些情况下，GPS信号的准确性和可靠性可能会降低。此外，当与惯性导航系统（INS）集成时，GPS/INS系统由于其累积误差，无法为中断问题提供长期解决方案。本文介绍了一种新颖的基于图的方法，该方法利用动态可调模糊窗口来提高导航和定位精度。这种方法有效地将GPS数据与视觉惯性里程计（VIO）集成在一起。此外，它还提出了一种用于运动估计和特征提取的新颖技术，称为自适应特征流融合，该技术有助于在具有高特征和低特征内容的环境中实现强大的性能。所提出的GPS/VIO系统是缓解基于GPS的导航的准确性和可靠性问题的一个引人注目的解决方案。它在Jetson嵌入式板平台上实现并进行了测试，以确保最佳的实时系统性能。本文全面详细地介绍了这些测试的结果。该系统使用KITTI数据集进行了严格评估，与扩展卡尔曼滤波器（EKF）系统相比，显示出显著的精度提高。具体而言，在持续10秒和27秒的GPS中断期间，GPS/VIO系统分别表现出84.59%和88.806%的显著精度提高。此外，为了提高精度，还采用了数据预处理技术。这些技术包括通过调整对比度和亮度水平来优化图像数据，并对惯性测量单元（IMU）数据应用降噪处理。这使得预定义轨迹的精度大幅提高了44.7%。
