AI 中文总结
本文提出一种基于关键时间高斯过程回归的实时事件相机立体视觉里程计，通过WNOA先验插值减少状态量，在MVSEC和DSEC上精度优于ES-PTAM。
AI 中文摘要
事件相机具有微秒级的时间分辨率和较高的动态范围，这使得它们比标准帧式相机更能抵抗运动模糊和光照不良的影响。当事件相机视觉里程计（VO）流程以原生时间分辨率处理异步事件流时，能够最大化这些优势。连续时间高斯过程（GP）回归和加速度白噪声（WNOA）先验可以处理异步测量，但在直接应用时会导致估计状态过大而难以处理。本文提出了一种连续时间事件相机立体视觉里程计流程，该流程在保持异步事件原生测量时间的同时，还能实时运行。它通过使用基于物理的WNOA先验，将测量插值到精确时间戳，从而将估计状态减少到关键时间，同时保持完整的时间分辨率。这在不丢弃异步特性的情况下，将状态大小与密集的测量数量解耦。该实时连续时间视觉里程计流程在MVSEC和DSEC数据集上进行了评估。在所有测试序列中，除了一个序列外，它提供的实时估计都比最先进的离散估计器ES-PTAM更准确。该流程在MVSEC和DSEC上分别以22 Hz和6 Hz的频率提供估计，并在所有有效序列上实现了0.46厘米和0.038度的RMS相对误差，分别比ES-PTAM好11倍和15倍。
英文摘要
Event cameras have microsecond-level temporal resolution and high dynamic range which make them more resilient to motion blur and poor illumination than standard frame-based cameras. Event-camera visual odometry (VO) pipelines maximize these benefits when they process the asynchronous event stream at the native temporal resolution. Continuous-time Gaussian process (GP) regression and a white-noise-on-acceleration (WNOA) prior can handle asynchronous measurements but result in a prohibitively large estimation state when applied naively. This paper presents a continuous-time event-camera stereo VO pipeline that maintains the native measurement times of asynchronous events while also running in real time. It reduces the estimation states to keytimes while maintaining full temporal resolution by interpolating measurements to their exact timestamps with a physically founded WNOA prior. This decouples the state size from the dense number of measurements without discarding their asynchronous nature. The real-time continuous-time VO pipeline is evaluated on the MVSEC and DSEC datasets. It provides estimates in real time that are more accurate than ES-PTAM, a state-of-the-art discrete estimator, in all but one of the tested sequences. The pipeline respectively provides estimates at 22 Hz and 6 Hz on MVSEC and DSEC and RMS relative errors of 0.46 cm and 0.038 degrees across all valid sequences, which were 11 and 15 times better than ES-PTAM, respectively.
CommentsSubmitted to IEEE International Conference on Robotics and Automation (ICRA) 2027, 8 pages, 1 figures, 3 tables. The corresponding video can be found at https://youtu.be/MmpH8QYR76g
