CARLA-Air：在CARLA世界中操控无人机——面向空天地一体化智能体的统一基础设施
CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
March 30, 2026
作者: Tianle Zeng, Hanxuan Chen, Yanci Wen, Hong Zhang
cs.AI
摘要
低空经济、具身智能与空地协同系统的融合，正催生对能够统一建模空中与地面智能体的仿真基础设施的需求。现有开源平台仍存在领域割裂：驾驶仿真器缺乏空中动力学支持，而多旋翼仿真器则缺少真实地面场景。基于桥接的联合仿真会引入同步开销，且无法保证严格的时空一致性。 本文提出CARLA-Air开源基础设施，在单一Unreal Engine进程中融合了高保真城市驾驶与物理精确的多旋翼飞行仿真。该平台完整保留CARLA与AirSim原生Python API及ROS 2接口，支持零修改代码复用。通过共享物理时钟与渲染管线，CARLA-Air可提供包含规则合规交通、社会意识行人及空气动力学一致无人机动态的逼真环境，并在每个时钟周期同步捕获全平台最多18种传感器模态数据。平台支持具身智能典型任务，包括协同作业、具身导航与视觉语言动作、多模态感知与数据集构建、强化学习策略训练等。可扩展资源管线支持将自定义机器人平台接入共享世界。通过继承AirSim（其上游开发已归档）的空中能力，CARLA-Air确保这一广泛应用的飞行技术栈能在现代基础设施中持续演进。 平台已发布预编译二进制包与完整源代码：https://github.com/louiszengCN/CarlaAir
English
The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency.
We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure.
Released with prebuilt binaries and full source: https://github.com/louiszengCN/CarlaAir