双图姿态图语义定位用于基于视觉的自主无人机竞速
Dual Pose-Graph Semantic Localization for Vision-Based Autonomous Drone Racing
AI总结：
本文提出双图姿态图架构，融合里程计与语义检测以实现稳健定位。通过临时图累积关键帧间观测并优化为单个约束，再推广至持久主图，提升信息丰富度并保持实时性能。
摘要：
自主无人机竞速需要在极端条件下实现鲁棒的实时定位：高速飞行、激进操作和受载荷限制的平台，通常依赖单个摄像头感知。现有视觉SLAM系统在一般场景中有效，但在竞速动态中面临运动模糊和特征不稳定问题，并未利用竞速环境的结构特性。本文提出双图姿态图架构，融合里程计与语义检测以实现稳健定位。临时图累积多个门观测并优化为单个约束，再推广至持久主图。该设计保留频繁检测的信息丰富度，同时防止图增长影响实时性能。系统设计为传感器无关，尽管本文使用单目视觉惯性里程计和视觉门检测进行验证。在TII-RATM数据集上的实验评估显示，与独立VIO相比，ATE减少了56%至74%。消融研究证实，双图架构在相同计算成本下比单图基线高10%至12%的精度。在A2RL竞赛中的部署表明，系统在飞行中实现实时机载定位，减少里程计基线漂移达每圈4.2米。
英文摘要：
Autonomous drone racing demands robust real-time localization under extreme conditions: high-speed flight, aggressive maneuvers, and payload-constrained platforms that often rely on a single camera for perception. Existing visual SLAM systems, while effective in general scenarios, struggle with motion blur and feature instability inherent to racing dynamics, and do not exploit the structured nature of racing environments. In this work, we present a dual pose-graph architecture that fuses odometry with semantic detections for robust localization. A temporary graph accumulates multiple gate observations between keyframes and optimizes them into a single refined constraint per landmark, which is then promoted to a persistent main graph. This design preserves the information richness of frequent detections while preventing graph growth from degrading real-time performance. The system is designed to be sensor-agnostic, although in this work we validate it using monocular visual-inertial odometry and visual gate detections. Experimental evaluation on the TII-RATM dataset shows a 56% to 74% reduction in ATE compared to standalone VIO, while an ablation study confirms that the dual-graph architecture achieves 10% to 12% higher accuracy than a single-graph baseline at identical computational cost. Deployment in the A2RL competition demonstrated that the system performs real-time onboard localization during flight, reducing the drift of the odometry baseline by up to 4.2 m per lap.
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。
