Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements
This addresses localization challenges for autonomous systems in urban environments, representing an incremental improvement over existing coupling approaches.
The paper tackles the problem of improving localization accuracy in visual-inertial SLAM by tightly coupling raw GNSS measurements, resulting in outperformance over state-of-the-art methods in complex urban scenes.
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge. More specifically, reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly minimized within a sliding window, in which the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are also addressed. Experimental results on public dataset in complex urban scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, including scenes mainly containing low-rise buildings and those containing urban canyons.