Event cameras are bio-inspired, motion-activated sensors that demonstrate impressive potential in handling challenging situations, such as motion blur and high-dynamic range. Despite their promise, existing event-based simultaneous localization and mapping (SLAM) approaches exhibit limited performance in real-world applications. On the other hand, state-of-the-art SLAM approaches that incorporate deep neural networks for better robustness and applicability. However, these is a lack of research in fusing learning-based event SLAM methods with IMU, which could be indispensable to push the event-based SLAM to large-scale, low-texture or complex scenarios. In this paper, we propose DEIO, the first monocular deep event-inertial odometry framework that combines learning-based method with traditional nonlinear graph-based optimization. Specifically, we tightly integrate a trainable event-based differentiable bundle adjustment (e-DBA) with the IMU pre-integration in a factor graph which employs keyframe-based sliding window optimization. Numerical Experiments in nine public challenge datasets show that our method can achieve superior performance compared with the image-based and event-based benchmarks. The source code is available at: https://github.com/arclab-hku/DEIO.

翻译：事件相机是一种受生物启发的运动激活传感器，在处理运动模糊和高动态范围等挑战性场景方面展现出巨大潜力。尽管前景广阔，现有基于事件的同时定位与建图（SLAM）方法在实际应用中性能有限。另一方面，当前最先进的SLAM方法通过融合深度神经网络获得了更强的鲁棒性和适用性。然而，将基于学习的事件SLAM方法与惯性测量单元（IMU）相融合的研究仍然匮乏，而这种融合对于推动事件SLAM应用于大规模、低纹理或复杂场景至关重要。本文提出DEIO，首个将基于学习的方法与传统非线性图优化相结合的单目深度事件-惯性里程计框架。具体而言，我们将可训练的事件相机可微分光束法平差（e-DBA）与IMU预积分紧密集成于采用基于关键帧滑动窗口优化的因子图中。在九个公开挑战数据集上的数值实验表明，相较于基于图像和基于事件的基准方法，本方法能实现更优越的性能。源代码已公开于：https://github.com/arclab-hku/DEIO。