Accurate navigation is required in many Unmanned Aerial Vehicle (UAV) applications. In recent years, GNSS Precise Point Positioning (PPP) has been recognised as an efficient approach for providing precise positioning services. In contrast to the widely used Real-Time Kinematic (RTK), PPP is independent of reference stations, which greatly broadens its scope of application. However, the accuracy and reliability of PPP can be significantly decreased by poor GNSS satellite geometry and outage. In response, a real-time four-constellation GNSS PPP is applied to improve the geometry in this work, and PPP is tightly coupled with an Inertial Measurement Unit (IMU) to smooth the position and velocity output, thus improving the robustness of the navigation solution. Experimental flight tests are carried out using a UAV in an open-sky area, and GNSS-challenged environments are simulated. The results show that the four-constellation GNSS PPP/IMU integration reduces the Root-Mean-Square (RMS) Three-Dimensional (3D) positioning and velocity error by 76.4% and 67.1%, respectively, in open sky with respect to the one-GNSS PPP. Under scenarios where GNSS measurements are insufficient, the coupled system can still provide continuous solutions. Moreover, the coupled PPP/IMU system can also maintain the convergence of PPP during GNSS-challenged periods and can greatly shorten the re-convergence period of PPP when the UAV returns to the open sky.

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

The complementary advantages of high-rate Global Positioning System (GPS) and accelerometer observations for measuring seismic ground motion have been recognised in previous research. Here we propose an approach of tight integration of GPS and accelerometer measurements. The baseline shifts of the accelerometer are introduced as unknown parameters and estimated by a random walk process in the Precise Point Positioning (PPP) solution. To demonstrate the performance of the new strategy, we carried out several experiments using collocated GPS and accelerometer. The experimental results show that the baseline shifts of the accelerometer are automatically corrected, and high precision coseismic information of strong ground motion can be obtained in real-time. Additionally, the convergence and precision of the PPP is improved by the combined solution.