Abstract: Systems and methods to automatically generate training data including airborne object trajectory data may receive real trajectory data from within a first environment, receive imaging data associated with a second environment, and transform and/or modify the real trajectory data to synthetic trajectory data for the second environment. Then, the synthetic trajectory data may be superimposed within the imaging data of the second environment. In addition, images of an airborne object may be rendered along the synthetic trajectory data to generate training data that may be used to train machine learning models or algorithms for various computer vision applications.

Abstract: Systems and methods to automatically generate training data for machine learning models may include an imaging device to capture imaging data, an image processing or rendering system to receive the imaging data and render a three-dimensional model of an object of interest overlaying the imaging data, an automatic mask extraction or generation system to extract or determine a mask, label, or annotation associated with the three-dimensional model and a plurality of pixels associated with the object of interest from a perspective of the imaging device, and a machine learning model to receive the imaging data and the mask as training data.

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).