Abstract: According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.

Abstract: According to various aspects, an obstacle map generator is provided, including: one or more processors configured to receive one or more depth images from a depth imaging system, determine, for each depth image of the one or more received depth images, a first set of pixels and a second set of pixels, each pixel of the first set of pixels has a depth value assigned thereto and each pixel of the second set of pixels has no depth value assigned thereto or has a depth value outside a predefined depth value range assigned thereto, assign a pre-defined depth value to one or more pixels of the second set of pixels, and generate an obstacle map based on the determined first set of pixels and the one or more pixels of the second set of pixels having the pre-defined depth value assigned thereto.

Abstract: System and techniques for drone obstacle avoidance using real-time wind estimation are described herein. A wind metric is measures at a first drone and communicated to a second drone. In response to receiving the wind metric, a flight plan of the second drone is modified based on the wind metric.

Abstract: According to various aspects, an obstacle map generator is provided, including: one or more processors configured to receive one or more depth images from a depth imaging system, determine, for each depth image of the one or more received depth images, a first set of pixels and a second set of pixels, each pixel of the first set of pixels has a depth value assigned thereto and each pixel of the second set of pixels has no depth value assigned thereto or has a depth value outside a predefined depth value range assigned thereto, assign a pre-defined depth value to one or more pixels of the second set of pixels, and generate an obstacle map based on the determined first set of pixels and the one or more pixels of the second set of pixels having the pre-defined depth value assigned thereto.

Abstract: According to various aspects, a collision avoidance method may include: receiving depth information of one or more depth imaging sensors of an unmanned aerial vehicle; determining from the depth information a first obstacle located within a first distance range and movement information associated with the first obstacle; determining from the depth information a second obstacle located within a second distance range and movement information associated with the second obstacle, the second distance range is distinct from the first distance range, determining a virtual force vector based on the determined movement information, and controlling flight of the unmanned aerial vehicle based on the virtual force vector to avoid a collision of the unmanned aerial vehicle with the first obstacle and the second obstacle.

Abstract: System and techniques for drone obstacle avoidance using real-time wind estimation are described herein. A wind metric is measures at a first drone and communicated to a second drone. In response to receiving the wind metric, a flight plan of the second drone is modified based on the wind metric.