MAVS provides the ability to evaluate the performance of autonomous perception and navigation software in real-time. The software is built with an MPI-based framework for coupling parallel processes, as well as a physics-based sensor simulator for LIDAR, GPS, cameras and other sensors.
While MAVS is a fully functional standalone simulator, additional wrappers allow MAVS to be integrated with robotic development tools such the Robotic Operating System (ROS).
A detailed view of a variety of factors that impact simulated environments.
Why use simulation for autonomous ground vehicle testing? The image above displays the many interactions that are involved in developing a simulation. Controlling these factors contributes greatly to experimental safety and cost effectiveness, repeatability, as well as overall documentation and statistics.
What importance does MAVS bring to simulation software?
While development is ongoing, the MAVS software is currently being used for applications including sensor placement, synthetic training-data for neural networks, and education efforts. MSU’s Halo Project also incorporates the MAVS software for autonomy system-level testing.
Screen captures from MAVS simulations that show the same environment with differing ecosystems (top) and with a change in terrain roughness (bottom).
Screen captures from MAVS software exhibiting the following variety of environment properties (from L to R): night, haze, snow, fog, dust, and rain.
The environment within MAVS can demonstrate a variety of custom ecosystems and natural properties, including snow, fog, dust and rain. Terrain properties and roughness can be controlled, as well as daylight brightness, haze, and moonlight.