Abstract: Provided are systems and techniques for automated navigation of vehicles, such as drones. The systems generally include processing unit(s) that, collectively, perform several steps. Such steps include generating metric depth estimates, using a pre-trained model, for each pixel in received image(s) from a monocular camera, or transformed image(s) based on the received image(s). Such steps may also include generating a pose estimate from visual odometry, then generating a truncated signed distance function representation of an environment based on the absolute depth estimates and the pose estimate. The steps may include creating and/or updating a local map based on the truncated signed distance function representation. The steps may include plan a collision-free route towards a goal based on the local map. This may include using motion primitives, which may be generated in a single offline step and stored in a trajectory library.

Abstract: Disclosed is a fast-response sensor system to measure a fluid vector in a plurality of dimensions. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), utilizes microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor system suitable for, e.g., real-time wind estimation onboard a UAV. The MAST uses three through eighteen polygonally-arranged microscale flow sensors to determine the fluid's vector's direction and magnitude.

Abstract: Disclosed is a fast-response sensor system to measure a fluid vector in a plurality of dimensions. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), utilizes microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor system suitable for, e.g., real-time wind estimation onboard a UAV. The MAST uses three through eighteen polygonally-arranged microscale flow sensors to determine the fluid's vector's direction and magnitude.