Dual Pose-Graph Semantic Localization for Vision-Based Autonomous Drone Racing

2026-04-16

AI Summary

This work addresses the challenge of insufficient robustness in real-time localization for high-speed, highly maneuverable drone racing, where motion blur, unstable visual features, and monocular vision constraints degrade performance. To this end, the authors propose a dual-pose-graph optimization framework that tightly integrates visual-inertial odometry with semantic gate detection cues. The approach employs a temporary graph to aggregate multi-view observations across keyframes, generating refined constraints that are subsequently incorporated into the main pose graph. This design effectively controls graph complexity without additional computational overhead, thereby balancing accuracy and real-time performance. Experimental results demonstrate a 56%–74% reduction in absolute trajectory error (ATE) on the TII-RATM dataset, yielding a 10%–12% accuracy improvement over single-graph baselines. In A2RL competition scenarios, the method reduces per-lap localization drift by up to 4.2 meters, enabling real-time onboard deployment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• autonomous drone racing
• visual localization
• motion blur
• semantic environment
• real-time performance

Innovation

• dual pose-graph
• semantic localization
• visual-inertial odometry
• autonomous drone racing
• graph optimization