“Autonomous navigation in unknown cluttered environments is one of the fundamental problems in robotics with applications in search and rescue, information gathering and inspection of industrial and civil structures, among others,” wrote the coauthors. “Although mapping, planning, and trajectory generation can be considered mature fields considering certain combinations of robotic platforms and environments, a framework combining elements from all these fields for [drone] navigation in general environments is still missing.”
The team’s algorithmic framework — which is designed for drones equipped with 3D sensors and odometry modules — comprises three components: (1) an algorithm that produces maps of the disparity between measurements obtained from the drone’s depth sensor, (2) a path generation model that takes into account field-of-view constraints on space that’s assumed to be safe for navigation, and (3) a model that generates robust motion plans. At the mapping stage, algorithms compute a point cloud from the disparity depth images and the odometry and add it to a map representation of the drone’s occupied space.
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