校正漂移的单目视觉惯性里程计与感知感知的自主无人机竞速
Drift-Corrected Monocular VIO and Perception-Aware Planning for Autonomous Drone Racing
AI总结：
本文提出了一种校正VIO漂移的方法，结合YOLO门检测器和卡尔曼滤波器，以提升无人机竞速中的视觉惯性里程计精度与感知规划性能。
摘要：
阿布扎比自主竞速联盟（A2RL）与无人机冠军联赛（DCL）比赛要求队伍仅使用单个摄像头和低质量惯性测量单元进行高速自主无人机竞速，这是一种最小化的传感器集，与专家人类无人机竞速飞行员相似。这种传感器限制使系统容易受到视觉惯性里程计（VIO）漂移的影响，特别是在长时间和高速飞行中进行激烈操作时。本文介绍了为比赛开发的系统，其在竞赛中表现出色。我们的方法通过将VIO输出与基于YOLO的门检测器生成的全局位置测量相结合，并使用卡尔曼滤波器来校正VIO漂移。一个感知感知的规划器生成了在速度与保持门可见性之间平衡的轨迹。该系统表现出色，在多个类别中均获得名次：在AI大奖赛中获得第三名，最高速度为43.2公里/小时；在AI拖车赛中获得第二名，速度超过59公里/小时；在AI多无人机赛中获得第二名。我们详细介绍了完整的架构，并基于比赛的实验数据进行了性能分析，贡献了关于构建基于单目视觉的自主无人机飞行成功系统的见解。
英文摘要：
The Abu Dhabi Autonomous Racing League(A2RL) x Drone Champions League competition(DCL) requires teams to perform high-speed autonomous drone racing using only a single camera and a low-quality inertial measurement unit -- a minimal sensor set that mirrors expert human drone racing pilots. This sensor limitation makes the system susceptible to drift from Visual-Inertial Odometry (VIO), particularly during long and fast flights with aggressive maneuvers. This paper presents the system developed for the championship, which achieved a competitive performance. Our approach corrected VIO drift by fusing its output with global position measurements derived from a YOLO-based gate detector using a Kalman filter. A perception-aware planner generated trajectories that balance speed with the need to keep gates visible for the perception system. The system demonstrated high performance, securing podium finishes across multiple categories: third place in the AI Grand Challenge with top speed of 43.2 km/h, second place in the AI Drag Race with over 59 km/h, and second place in the AI Multi-Drone Race. We detail the complete architecture and present a performance analysis based on experimental data from the competition, contributing our insights on building a successful system for monocular vision-based autonomous drone flight.
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。
