用单目相机实现高空自主导航与语义地图构建，支持自然语言任务规划。
HALO: High-Altitude Language-Conditioned Monocular Aerial Exploration and Navigation
- 基于单目相机+GPS+IMU，实现实时远距离三维重建。
- 在78,000平方米环境中探索效率比基线高68%。
- 适合无人机自主执行复杂室外任务，可部署于真实飞行平台。
我们展示了使用单目相机配合全球定位系统（GPS）和惯性测量单元（IMU），在高空实时完成度量-语义地图构建与探索。系统名为HALO，解决了两大挑战：（i）远距离视觉下的实时稠密3D重建；（ii）大尺度户外环境的高精度几何与语义地图构建与探索。我们证明，HALO能够规划信息丰富的路径，利用该信息完成多任务自然语言指令。在最大面积达78,000平方米的大规模环境仿真评估中，HALO始终以更少探索时间完成任务，并在行驶距离方面较当前最优语义探索基线提升最高68%的竞争力比。通过自研四轴飞行器平台的真实实验，验证了（i）所有模块可在机载运行，（ii）在多种环境下，可支持覆盖面积达24,600平方米、飞行高度40米的任务自主执行。更多视频与细节见项目页：https://tyuezhan.github.io/halo/。
原文摘要 · Abstract (English)
We demonstrate real-time high-altitude aerial metric-semantic mapping and exploration using a monocular camera paired with a global positioning system (GPS) and an inertial measurement unit (IMU). Our system, named HALO, addresses two key challenges: (i) real-time dense 3D reconstruction using vision at large distances, and (ii) mapping and exploration of large-scale outdoor environments with accurate scene geometry and semantics. We demonstrate that HALO can plan informative paths that exploit this information to complete missions with multiple tasks specified in natural language. In simulation-based evaluation across large-scale environments of size up to 78,000 sq. m., HALO consistently completes tasks with less exploration time and achieves up to 68% higher competitive ratio in terms of the distance traveled compared to the state-of-the-art semantic exploration baseline. We use real-world experiments on a custom quadrotor platform to demonstrate that (i) all modules can run onboard the robot, and that (ii) in diverse environments HALO can support effective autonomous execution of missions covering up to 24,600 sq. m. area at an altitude of 40 m. Experiment videos and more details can be found on our project page: https://tyuezhan.github.io/halo/.
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