Hang Zhao

One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished accuracy during GNSS signal loss, we propose a GNSS/IMU/ODO integrated navigation method based on an adaptive sliding window factor graph. The measurements from the ODO are utilized as observation factors to mitigate prediction interpolation errors associated with traditional ODO pre-integration methods. Additionally, online estimation and compensation for both installation angle deviations and scale factors of the ODO further enhance its ability to constrain pose errors during GNSS signal loss. A multi-state marginalization algorithm is proposed and then utilized to adaptively adjust the sliding window size based on the quality of GNSS observations, enhancing pose optimization accuracy in multi-source fusion while prioritizing computational efficiency. Tests conducted in typical urban environments and mountainous regions demonstrate that our proposed method significantly enhances fusion navigation accuracy under complex GNSS conditions. In a complex city environment, our method achieves a 55.3% and 29.8% improvement in position and velocity accuracy and enhancements of 32.0% and 61.6% in pitch and heading angle accuracy, respectively. These results match the precision of long sliding windows, with a 75.8% gain in computational efficiency. In mountainous regions, our method enhances the position accuracy in the three dimensions by factors of 89.5%, 83.7%, and 43.4%, the velocity accuracy in the three dimensions by factors of 65.4%, 32.6%, and 53.1%, and reduces the attitude errors in roll, pitch, and yaw by 70.5%, 60.8%, and 26.0%, respectively, demonstrating strong engineering applicability through an optimal balance of precision and efficiency.

Autonomous positioning in complex urban areas is a challenging problem and has been attracted increasing attention in academia. Multi-sensor fusion of camera, Inertial measurement Unit and wheel odometer has been a prevailing solution that is not only low-cost but easy-to-build. Most of the related methods fuse the odometer measurements into the visual-inertial framework by pre-integrating the wheel speed into the displacement. However, they are not very suitable for some scenarios where the vehicle velocity changes frequently. Instead of pre-integrating the odometer measurements, in this paper, we present an efficient and practical visual-inertial-odometer approach that fuses the wheel speed into the visual-inertial framework directly based on the vehicle motion constraint. Specifically, we use the non-holonomic constraint and lever-arm compensation to introduce the vehicle velocity measurement into the fusion framework to limit the drift. Meanwhile, the proposed framework also allows to online calibrate the IMU-odometer extrinsic parameters explicitly. Moreover, we develop an observability-aware method to enhance the system stability and the performance of online extrinsic calibration. We also develop a robust initialization method to obtain the initial values of visual-inertial-odometer system in one-step optimization. Our approach is validated extensively on simulation environment, autonomous driving public dataset and real world experiment. The simulation results show the extrinsic calibration error is within 0.1 degrees/0.02 m for rotation and translation. The public dataset and real-world ground robot experiments shows a 0.3% position error in both 4 km long urban area route and 403 m long park route.