面向行星探测的实时异步单目里程计设计
Design of a Real-time Asynchronous Monocular Odometry for Planetary Exploration
AI总结：
针对行星探测中计算资源受限、环境复杂且高动态范围光照的挑战，提出一种基于误差状态卡尔曼滤波（ESKF）的实时异步事件相机单目里程计，利用异步事件流和RATE特征跟踪器实现连续相机运动估计。
摘要：
我们描述了面向行星探测的实时异步事件基单目里程计的初步设计。在严格的计算约束下运行，行星探测器经常遇到复杂、不可预测的环境，需要高速感知和对高动态范围（HDR）光照的鲁棒性。事件相机通过报告异步、像素级的亮度变化（微秒级分辨率）来满足这些需求，在极端光照条件下显著降低数据带宽同时保持鲁棒性。我们提出了一种基于误差状态卡尔曼滤波（ESKF）的方法，利用异步事件流连续估计相机自运动。相机状态通过RATE（一种实时异步特征跟踪器）生成的每个跟踪位置输出进行更新。
英文摘要：
We describe our preliminary design of a real-time asynchronous event-based monocular odometry for planetary exploration. Operating under strict computational constraints, planetary rovers frequently encounter complex, unpredictable environments that demand high-speed sensing and robustness to high dynamic range (HDR) lighting. Event cameras address these needs by reporting asynchronous, pixel-wise brightness changes with microsecond resolution, significantly reducing data bandwidth while maintaining robustness in extreme lighting conditions. We propose an approach based on an Error-State Kalman Filter (ESKF) that leverages this asynchronous event stream to continuously estimate camera ego-motion. The camera state is updated with every tracked position output generated by RATE, a real-time asynchronous feature tracker.
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