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.

翻译：视觉-惯性里程计（VIO）因其低成本且互补的传感器配置已展现出显著成功。然而，现有VIO方法普遍缺乏适应不同环境与传感器特性的泛化能力。本文提出自适应VIO——一种将在线持续学习与传统非线性优化相结合的新型单目视觉-惯性里程计系统。该框架包含两个分别预测视觉对应关系与IMU偏差的网络。与采用端到端网络直接融合相机和IMU双模态特征以预测位姿的方法不同，本系统将神经网络与视觉-惯性光束法平差相结合。优化后的估计值将反馈至视觉网络与IMU偏差网络，以自监督方式持续优化网络参数。这种学习-优化协同框架与反馈机制使系统能够实现在线持续学习。实验表明，我们的自适应VIO在EuRoC和TUM-VI数据集上展现出卓越的环境适应能力。其综合性能超越当前已知的基于学习的VIO方法，并与最先进的基于优化的方法相媲美。