AirSim (Aerial Informatics and Robotics Simulation) is an open-source simulator developed by Microsoft for AI research in autonomous vehicles, including drones and cars. Built on Unreal Engine, AirSim offers high-fidelity physics and photo-realistic environments, making it ideal for developing and validating algorithms for perception, control, and reinforcement learning.

AirSim is widely used in academia and industry for tasks like autonomous navigation, collision avoidance, and multi-agent coordination, and supports integration with popular machine learning and robotics tools.

Key benefits:

• Realistic simulation of sensors and environments

• Support for aerial and ground vehicles

• Open-source and extensible for custom research

What are the main features of AirSim?

Photo-realistic environments with real-time physics

AirSim leverages Unreal Engine to create immersive 3D worlds that closely replicate real-world scenarios.

• High-resolution textures, lighting, and materials

• Configurable weather, time of day, and environmental complexity

• Realistic physics for vehicle motion, collision, and terrain interaction

Accurate sensor simulation

AirSim includes a wide range of simulated sensors to support research in computer vision, SLAM, and sensor fusion.

• RGB, depth, and segmentation cameras

• LiDAR, IMU, GPS, magnetometer, barometer

• Configurable noise models and sampling rates

Support for both drones and ground vehicles

The platform allows simulation of quadrotors and wheeled vehicles under various control schemes.

• Built-in flight and car control APIs

• Support for multi-vehicle scenarios

• Physics-based tuning for realistic behavior

Integration with ML and robotics frameworks

AirSim supports real-time control and data streaming, enabling training and testing of AI models.

• Python and C++ APIs for simulation control

• ROS integration for robotic system compatibility

• Data logging for supervised and reinforcement learning workflows

Extensible and community-driven

As an open-source project under the MIT license, AirSim is adaptable to different research needs.

• Customizable environments, vehicle models, and sensor configurations

• Active community and Microsoft-led development

• Compatible with training pipelines for deep learning and reinforcement learning

Why choose AirSim?

• Designed for autonomous systems research, especially drones and self-driving cars

• High-fidelity environments and realistic sensor models, suitable for vision-based AI

• Cross-platform and open-source, easily customizable

• Compatible with AI and robotics toolchains, including ROS and ML libraries

• Widely adopted in academia and industry, supporting reproducible simulation-based research