Yan Zhang
ABSTRACT
For complex environments, there is a problem of cumulative error in VIO algorithms based on vision and IMU. Therefore, this paper proposes a multi-source visual SLAM localization model that reuses IMU data. Firstly, integrating visual and IMU Data based on graph optimization, integrating IMU and GNSS Data based on EKF algorithms, through the above two methods, two primary positioning results are calculated. Then, two primary positioning results are integrated to output the final positioning results, and actual vehicle tests are conducted. The experimental results show that using the EKF algorithm can filter GNSS data with significant errors on the one hand; On the other hand, it can increase the frequency of GNSS data, reduce the error caused by timestamp matching, and thus improve the positioning accuracy of multi-source visual SLAM positioning models that reuses IMU data. Compared to directly using 1Hz original carrier differential fixed solution GNSS data, the RMS of positioning error which using filtered GNSS positioning data with the same frequency as vision is reduced by 90.04%.
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Digital Library
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Index Terms
- A Multi-source Visual SLAM Localization Algorithm That Reuses IMU Data
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