Adaptive Fusion Positioning Based on Gaussian Mixture Model for GNSS-RTK and Stereo Camera in Arboretum Environments

Abstract

The integration of Global Navigation Satellite System (GNSS) Real-Time Kinematics (RTK) can provide high-precision, real-time, and global coverage of location information in open areas. But in arboretum environment, the ability to achieve continuous high-precision positioning using global positioning technology is limited due to various sources of interference, such as multi-path effects, signal obstruction, and environmental noise. In order to achieve precise navigation in challenging GNSS signal environments, visual SLAM systems are widely used due to their ability to adapt to different environmental features. Therefore, this paper proposes an optimized solution that integrates the measurements from GNSS-RTK and stereo cameras. The presented approach aligns the coordinates between the two sensors, and then employs an adaptive sliding window approach, which dynamically adjusts the window size and optimizes the pose within the sliding window. At the same time, to address the variations and uncertainties of GNSS signals in non-ideal environments, this paper proposes a solution that utilizes a Gaussian Mixture Model (GMM) to model the potential noise in GNSS signals. Furthermore, it employs a Variational Bayesian Inference-based (VBI) method to estimate the parameters of the GMM model online. The integration of this model with an optimization-based approach enhances the positioning accuracy and robustness even further. The evaluation results of real vehicle tests show that in challenging GNSS arboretum environments, GMM applied to GNSS/VO integration has higher accuracy and better robustness.