Abstract
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.