An interesting group from Beijing, China initiated an open source autonomous aviation project for drones and passenger-carrying aerial vehicles called Generalized Autonomy Aviation System (GAAS)
The team strives to provide a common infrastructure for drone autonomy and to accelerate the use of computer vision on drones. This is how they’re describing what they have accomplished and is working on:
"The project is continuously adding algorithms to increase the level of autonomy and to expand the capabilities of drones. Some of the existing features are static obstacle avoidance with stereo cameras, route navigation, 3D reconstruction, image detection, object tracking, precision landing on moving projects, image segmentation and more. Though GAAS provides algorithms for a wide range of features, its goal is to act as an easy-to-use communication architecture between PX4 and algorithms. GAAS utilizes stereo cameras and Simultaneous Localization and Mapping (SLAM) technology for collision avoidance and navigation. Stereo camera has become more and more popular in the drone community because of its affordable price and versatility. While the implementation of LiDAR on UAVs is typically limited to 16-line, stereo cameras are capable of providing depth maps with much higher resolution for collision avoidance. Along with stereo cameras, SLAM technology provides to drones the ability to be aware of its location in the map, and thus allowing drones to navigate to a specific landing location by itself. "
A set of Quick Start tutorials is also available at https://github.com/generalized-intelligence/GAAS/tree/master/demo
Would love some general feedback from the community:
*would you use it?
*does this overlap with PX4/avoidance?