Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to unseen environments, methods for continual adaptation during inference time are of significant interest. In this work, we introduce CoVIO for online continual learning of visual-inertial odometry. CoVIO effectively adapts to new domains while mitigating catastrophic forgetting by exploiting experience replay. In particular, we propose a novel sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices. We further provide an asynchronous version that decouples the odometry estimation from the network weight update step enabling continuous inference in real time. We extensively evaluate CoVIO on various real-world datasets demonstrating that it successfully adapts to new domains while outperforming previous methods. The code of our work is publicly available at http://continual-slam.cs.uni-freiburg.de.

翻译：视觉里程计是移动设备和机器人平台上诸多应用的基础任务。由于此类应用通常不限于预定义的目标域，且基于学习的视觉系统在未知环境中泛化能力较差，因此推理阶段的持续适应方法具有重要意义。本文提出了面向视觉-惯性里程计的在线持续学习框架CoVIO，通过经验回放机制高效适应新领域，同时缓解灾难性遗忘问题。具体而言，我们设计了一种新颖的采样策略，在固定大小的回放缓冲区中最大化图像多样性，以满足嵌入式设备有限存储容量的需求。此外，我们提出了异步版本，将里程计估计与网络权重更新步骤解耦，从而实现实时连续推理。在多个真实世界数据集上的广泛评估表明，CoVIO在成功适应新领域的同时，性能优于先前方法。本工作代码已公开于 http://continual-slam.cs.uni-freiburg.de。