A Dynamic Visual-Inertial-Wheel Odometry With Semantic Constraints and Denoised IMU-Odometer Prior for Autonomous Driving

Currently, most visual-based simultaneous localization and mapping (VSLAM) algorithms assume that the environment is largely static, while the scenes surrounding autonomous vehicles are usually highly dynamic. Therefore, this assumption greatly limits the application of VSLAM in autonomous driving. In this work, we propose a novel tightly coupled monocular visual-inertial-wheel odometry framework, called Dyna-VIWO, which is robust against high-dynamic urban environments for autonomous vehicles. In this framework, we first propose a robust adaptive method for adjusting the residual weights of potential dynamic features, by jointly considering semantic constraints and pose prior estimated by the denoised IMU-Odometer preintegration. Then, a new system abnormality detection algorithm and state update strategy are proposed to ensure the smoothness and accuracy of pose estimation in highly dynamic scenes. In particular, the proportion of dynamic points participated in the optimizer serves as one of the main indicators for system anomaly judgment, and we develop a new dynamic point recognition algorithm based on semantic prior and geometric constraints between consecutive frames. Finally, we evaluate our method on a public dataset containing a large number of dynamic objects. The results show that our proposed method can significantly improve the robustness and accuracy of the system compared to the state-of-the-art VIO/VIWO methods in highly dynamic environments.