# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 1 Introduction
[15] proposed that the feature points in images with known world coordinate systems were regarded as \"visual pseudo-satellites\", and they could be tightly combined with GNSS ranging signals to realize continuous navigation in weak GNSS environments. In the literature, the authors of [16; 17] utilized a tight combination in the case of insufficient satellite observations to carry out navigation research on a combined GNSS and vision system. In conclusion, there have been more studies related to the combination and assistance of vision and GNSS. However, most of these studies were based on vision/GNSS loosely coupled or tightly coupled in general frameworks, and this required GNSS signals to be present for a long time to be used for navigation and localization; therefore, these are not applicable to cases where GNSS is not available or only opportunistic signals are present. To address the above problems, we used intermittently available GNSS positioning results as constraint data to assist with vision for localization, realizing that the global position of an unmanned carrier can still be obtained when GNSS data are not available. Specifically, we made the following contributions: we proposed a general framework for GNSS-assisted visual dynamic localization, stating that the overall process consisted of five parts (initialization, a GNSS coordinate system and world coordinate system conversion, frame sequence tracking and position and orientation estimation, local optimization, and frame sequence GNSS coordinate-solving). We took three frames of images and their corresponding GNSS coordinates as the constraint data, and we calculated the transformation matrix between the GNSS coordinate system and the world coordinate system through the Horn coordinate transformation. Then, the relative position and orientation relations of the image sequence in the world coordinate system could be obtained using epipolar geometry constraints, homography transformation, PNP (perspective-n-point) positions, and orientation solving. Finally, the position parameters of the carrier in the GNSS coordinate system under the GNSS rejection were obtained. We validated this study's algorithm based on a dataset and the measured data, respectively. The validation results showed that the algorithm was applicable to the case of the presence of intermittent GNSS opportunity signals, and it could provide global position services with high short-term positioning accuracy. However, due to the scale drift of the visual odometer itself, this algorithm was not applicable to the case of complete GNSS signal rejection when the visual localization results could not be constrained and corrected. Then, we further compared the errors of the GNSS initial-assisted positioning and GNSS continuous-assisted positioning, and the results showed that the accuracy of the GNSS continuous-assisted positioning was two to three times higher than that of the GNSS initial-assisted positioning, which proved that the GNSS continuous-assisted positioning algorithm could maintain positioning accuracy for a long time, and it had good reliability and applicability in unknown environments. The rest of this paper is organized as follows. The framework of the GNSS-assisted visual dynamic positioning method is given in Section 2. In Section 3, the algorithm flow is established, including initialization, the GNSS coordinate system, the world coordinate system conversion, the frame sequence tracking and position and orientation estimation, the local optimization, and the frame sequence GNSS coordinate-solving. In Section 4, through dataset validation, the applicability of this paper's algorithm is demonstrated. In Section 5, through discussing our experiments with real data, it is further verified that the GNSS continuous-assisted algorithm improved the accuracy by a factor of two to three with respect to the GNSS initial-assisted algorithm. Finally, conclusions and further research arrangements are drawn in Section 6.