DEIO：深度事件惯性里程计
DEIO: Deep Event Inertial Odometry
AI总结：
提出首个基于学习的单目事件-惯性里程计框架DEIO，结合事件循环网络与IMU，利用可微束调整的Hessian信息优化因子图，在10个公共基准上超越20多种最先进方法。
摘要：
事件相机在处理快速运动和高动态范围等挑战性场景下的视觉里程计（VO）方面展现出巨大潜力。尽管前景广阔，但事件数据的稀疏性和运动依赖性特征，在实际应用中持续限制了基于特征或基于直接法的数据关联方法的性能。为了解决这些局限性，我们提出了深度事件惯性里程计（DEIO），这是首个基于学习的单目事件-惯性框架，它将基于学习的方法与传统的基于图的非线性优化相结合。具体而言，采用基于事件的循环网络来提供事件块随时间变化的准确且稀疏的关联。DEIO 进一步将其与 IMU 集成，以恢复带尺度的位姿并提供鲁棒的状态估计。利用从可微束调整（DBA）导出的 Hessian 信息来优化共视图因子图，该图在基于关键帧的滑动窗口内紧密融合了事件块对应关系和 IMU 预积分。综合验证表明，与超过 20 种最先进的方法相比，DEIO 在 10 个具有挑战性的公共基准上取得了卓越的性能。
英文摘要：
Event cameras show great potential for visual odometry (VO) in handling challenging situations, such as fast motion and high dynamic range. Despite this promise, the sparse and motion-dependent characteristics of event data continue to limit the performance of feature-based or direct-based data association methods in practical applications. To address these limitations, we propose Deep Event Inertial Odometry (DEIO), the first monocular learning-based event-inertial framework, which combines a learning-based method with traditional nonlinear graph-based optimization. Specifically, an event-based recurrent network is adopted to provide accurate and sparse associations of event patches over time. DEIO further integrates it with the IMU to recover up-to-scale pose and provide robust state estimation. The Hessian information derived from the learned differentiable bundle adjustment (DBA) is utilized to optimize the co-visibility factor graph, which tightly incorporates event patch correspondences and IMU pre-integration within a keyframe-based sliding window. Comprehensive validations demonstrate that DEIO achieves superior performance on \textit{10} challenging public benchmarks compared with more than 20 state-of-the-art methods.
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