Deep State-Space Monocular Visual Odometry with Learnable Kalman Filtering
TL;DR
A monocular visual odometry method that combines deep temporal features with a Kalman filtering module based on a state-space model, which improves pose estimation robustness and adaptability to incomplete observations and verifies the effectiveness of combining classical filtering theory with deep learning for monocular visual odometry.
Abstract
Monocular visual odometry is important in autonomous driving, robotics, and related fields, and has attracted increasing attention in computer vision.Traditional geometric methods and end-to-end deep learning methods have achieved promising results in monocular visual odometry, but they still face limitations in temporal consistency, uncertainty representation, and abnormal observation handling.To address these issues, this paper proposes a monocular visual odometry method that combines deep temporal features with a Kalman filtering module based on a state-space model. By explicitly modeling the temporal evolution of motion states in the state space, the method introduces continuity constraints into the pose estimation process. At the same time, the state transition matrix, as well as the process noise covariance matrix and the measurement noise covariance matrix, are learned by neural networks, which enables the system to adaptively adjust the fusion weight between prediction and observation. Experimental results on the KITTI VO dataset show that, compared with the baseline method, the proposed model improves pose estimation robustness and adaptability to incomplete observations, which verifies the effectiveness of combining classical filtering theory with deep learning for monocular visual odometry.