Learning Kalman Network: A deep monocular visual odometry for on-road driving

Zhao, Cheng, Sun, Li, Yan, Zhi , Neumann, Gerhard, Duckett, Tom and Stolkin, Rustam (2019) Learning Kalman Network: A deep monocular visual odometry for on-road driving. Robotics and Autonomous Systems, 121 . p. 103234. ISSN 0921-8890

Full content URL: https://doi.org/10.1016/j.robot.2019.07.004

Learning Kalman Network: A deep monocular visual odometry for on-road driving
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This paper proposes a Learning Kalman Network (LKN) based monocular visual odometry (VO), i.e. LKN-VO, for on-road driving. Most existing learning-based VO focus on ego-motion estimation by comparing the two most recent consecutive frames. By contrast, the LKN-VO incorporates a learning ego-motion estimation through the current measurement, and a discriminative state estimator through a sequence of previous measurements. Superior to the model-based monocular VO, a more accurate absolute scale can be learned by LKN without any geometric constraints. In contrast to the model-based Kalman Filter (KF), the optimal model parameters of LKN can be obtained from dynamic and deterministic outputs of the neural network without elaborate human design. LKN is a hybrid approach where we achieve the non-linearity of the observation model and the transition model though deep neural networks, and update the state following the Kalman probabilistic mechanism. In contrast to the learning-based state estimator, a sparse representation is further proposed to learn the correlations within the states from the car’s movement behaviour, thereby applying better filtering on the 6DOF trajectory for on-road driving. The experimental results show that the proposed LKN-VO outperforms both model-based and learning state-estimator-based monocular VO on the most well-cited on-road driving datasets, i.e. KITTI and Apolloscape. In addition, LKN-VO is integrated with dense 3D mapping, which can be deployed for simultaneous localization and mapping in urban environments.

Keywords:mobile robotics, SLAM, dead reckoning, Monocular visual odometry, Learning Kalman Filter, Vehicle driving
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
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ID Code:43351
Deposited On:14 Dec 2020 11:54

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