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[bibtex]@InProceedings{Pan_2024_CVPR, author = {Pan, Youqi and Zhou, Wugen and Cao, Yingdian and Zha, Hongbin}, title = {Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {18019-18028} }
Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning
Abstract
Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper we propose Adaptive VIO a new monocular visual-inertial odometry that combines online continual learning with traditional nonlinear optimization. Adaptive VIO comprises two networks to predict visual correspondence and IMU bias. Unlike end-to-end approaches that use networks to fuse the features from two modalities (camera and IMU) and predict poses directly we combine neural networks with visual-inertial bundle adjustment in our VIO system. The optimized estimates will be fed back to the visual and IMU bias networks refining the networks in a self-supervised manner. Such a learning-optimization-combined framework and feedback mechanism enable the system to perform online continual learning. Experiments demonstrate that our Adaptive VIO manifests adaptive capability on EuRoC and TUM-VI datasets. The overall performance exceeds the currently known learning-based VIO methods and is comparable to the state-of-the-art optimization-based methods.
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