立体事件相机的深度视觉里程计
Deep Visual Odometry for Stereo Event Cameras
AI总结：
本文提出Stereo-DEVO，一种基于深度学习的立体事件视觉里程计，通过静态-立体关联策略实现稀疏深度估计，并集成紧耦合BA优化，在公制尺度下实现高精度实时位姿估计，优于现有方法，且能在夜间HDR场景中稳定运行。
摘要：
事件相机是一种受生物启发的传感器，其像素独立且异步地以微秒级分辨率响应亮度变化，这使其具备处理涉及运动模糊和高动态范围（HDR）光照条件的状态估计任务的潜力。然而，依赖于手工设计的数据关联（无论是直接方法还是间接方法）的事件视觉里程计（VO）的通用性仍然不可靠，尤其是在低光照HDR条件下的野外机器人应用中，此时动态范围可能极大，且信噪比在空间和时间上均发生变化。利用深度神经网络为克服这些挑战提供了新的可能性。在本文中，我们提出了一种基于学习的立体事件视觉里程计。基于深度事件视觉里程计（DEVO），我们的系统（称为Stereo-DEVO）引入了一种新颖且高效的静态-立体关联策略，用于稀疏深度估计，且几乎不增加额外的计算负担。通过将其集成到紧耦合的光束法平差（BA）优化方案中，并利用循环网络通过基于体素的事件表示进行精确光流估计以建立可靠的块关联的能力，我们的系统实现了公制尺度下的高精度位姿估计。与DEVO的离线性能相比，我们的系统能够实时处理视频图形阵列（VGA）分辨率的事件数据。在多个公开的真实世界数据集和自采集数据上的广泛评估验证了我们系统的通用性，展示了其相比最先进的事件VO方法的优越性能。更重要的是，我们的系统即使在大规模夜间HDR场景中也能实现稳定的位姿估计。
英文摘要：
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HDR conditions, where the dynamic range can be enormous and the signal-to-noise ratio is spatially-and-temporally varying. Leveraging deep neural networks offers new possibilities for overcoming these challenges. In this paper, we propose a learning-based stereo event visual odometry. Building upon Deep Event Visual Odometry (DEVO), our system (called Stereo-DEVO) introduces a novel and efficient static-stereo association strategy for sparse depth estimation with almost no additional computational burden. By integrating it into a tightly coupled bundle adjustment (BA) optimization scheme, and benefiting from the recurrent network's ability to perform accurate optical flow estimation through voxel-based event representations to establish reliable patch associations, our system achieves high-precision pose estimation in metric scale. In contrast to the offline performance of DEVO, our system can process event data of \zs{Video Graphics Array} (VGA) resolution in real time. Extensive evaluations on multiple public real-world datasets and self-collected data justify our system's versatility, demonstrating superior performance compared to state-of-the-art event-based VO methods. More importantly, our system achieves stable pose estimation even in large-scale nighttime HDR scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
