基于自适应衰减时间表面与极性感知跟踪的单目事件-惯性里程计
Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking
AI总结：
本文提出一种结合自适应衰减时间表面与极性感知跟踪的单目事件-惯性里程计，通过引入极性反转时间表面解决运动方向变化导致的极性翻转问题，在多种数据集上优于现有最先进技术。
摘要：
事件相机因在低功耗、高动态范围和无运动模糊方面优于传统相机而备受关注。本文提出了一种单目事件-惯性里程计，结合了基于自适应衰减核的时间表面与极性感知跟踪。我们利用基于自适应衰减的时间表面从异步事件中提取纹理信息，该表面能够适应事件流的动态特性并增强环境纹理的表示。然而，极性加权时间表面在运动方向变化时会受到事件极性翻转的影响。为了减轻其对特征跟踪的不利影响，我们通过引入额外的极性反转时间表面来优化特征跟踪，以增强鲁棒性。与视觉-惯性及事件-惯性里程计方法的对比分析表明，我们的方法优于现有最先进技术，在多种数据集上取得了具有竞争力的结果。
英文摘要：
Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odometry incorporating an adaptive decay kernel-based time surface with polarity-aware tracking. We utilize an adaptive decay-based Time Surface to extract texture information from asynchronous events, which adapts to the dynamic characteristics of the event stream and enhances the representation of environmental textures. However, polarity-weighted time surfaces suffer from event polarity shifts during changes in motion direction. To mitigate its adverse effects on feature tracking, we optimize the feature tracking by incorporating an additional polarity-inverted time surface to enhance the robustness. Comparative analysis with visual-inertial and event-inertial odometry methods shows that our approach outperforms state-of-the-art techniques, with competitive results across various datasets.
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