ビジョンベース自律ドローンレースのための二重ポーズグラフ意味的ローカライゼーション
Dual Pose-Graph Semantic Localization for Vision-Based Autonomous Drone Racing
ドローンレースの過酷な条件下で、オドメトリと意味的検出を融合した二重ポーズグラフ構造により、ロバストなリアルタイム自己位置推定を実現した。
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著者: David Perez-Saura, Miguel Fernandez-Cortizas, Alvaro J. Gaona, Pascual Campoy
分類: cs.RO
原文アブストラクト
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.
