发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TinyCVIO通过LED星座辅助和刚性板测量模型，在纳米无人机上实现低延迟高精度视觉惯性里程计，显著降低轨迹误差。
AI 中文摘要
纳米无人机在严苛的感知和计算约束下需要精确、实时的状态估计。我们提出了TinyCVIO，一个视觉惯性里程计系统，它协同设计了微型传感、视觉处理和估计，适用于具有520 kB SRAM的商用双核微控制器。轻量级LED星座提供已知几何结构，无需测量位置或偏航角，假设它们放置在同一水平面上。一个流式视觉前端以29.2 FPS跟踪来自毫米级相机的LED观测，而刚性板测量模型保留了LED间约束，流式QR码边界估计工作空间以保持固定滤波器状态大小。在19个手持硬件在环数据集中，刚性板模型相对于平面点将平均绝对轨迹误差降低了27%。完整系统在Crazyflie上以三种速度的九次飞行中机载运行，实现了3.5-3.7 cm的平均绝对轨迹误差和10 m段上0.50-0.60%的相对位姿误差，平均估计延迟为15.7-16.3 ms。
英文摘要
Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odometry system that co-designs miniature sensing, visual processing, and estimation for a commodity dual-core microcontroller with 520 kB SRAM. Lightweight LED constellations provide known geometry without surveyed positions or yaw angles, assuming placement on a common level plane. A streaming visual frontend tracks LED observations from a millimeter-scale camera at 29.2 FPS, while a rigid-board measurement model retains inter-LED constraints and streaming QR bounds estimation workspace for a fixed filter-state size. Across 19 hand-held hardware-in-the-loop datasets, the rigid-board model reduces mean absolute trajectory error by 27% relative to planar points. The complete system runs onboard a Crazyflie across nine flights at three speeds, achieving 3.5-3.7 cm mean absolute trajectory error and 0.50-0.60% relative pose error over 10 m segments, with mean estimate latency of 15.7-16.3 ms.
Comments9 pages, 6 figures, 4 tables. Video: https://youtu.be/6hQNdIcjHJE
