Abstract: Data for training a machine learning algorithm to detect airborne objects is generated by capturing background images using a camera aboard an aerial vehicle, and generating a trajectory of a synthetic object, such another aerial vehicle, in three-dimensional space. The trajectory is projected into an image plane of the camera, determined from a pose of the camera calculated using inertial measurement unit data captured by the aerial vehicle. Images of the synthetic object may be rendered based on locations of the trajectory in the image plane at specific times. Pixel-accurate locations for the rendered images may be determined by calculating a homography from consecutive images captured using the camera, and adjusting locations of the trajectory using the homography. The rendered images may be blended into the images captured by the camera at such locations, and used to train a machine learning algorithm.

Abstract: Systems and methods are provided herein for managing a set of autonomous vehicles (AVs) configured to perform delivery tasks and computing tasks. Computing tasks can be performed such as training a model and/or calculating an incremental update for the model. As additional training data is obtained, a subset of AVs may be managed as a distributed computing cluster and assigned a computing task such as training or calculating an incremental update for the model or any suitable computing task. Corresponding data computed by the subset of AVs of the cluster (e.g., the retrained model, updated model parameters corresponding to the updated model, etc.) may be received and stored or transmitted (e.g., the computing task requestor, to the AVs, etc.) for subsequent use (e.g., for subsequent delivery tasks).