Abstract: Systems and methods for performing three-dimensional multi-object tracking are disclosed herein. In one example, a method includes the steps of determining a residual based on augmented current frame detection bounding boxes, augmented previous frame detection bounding boxes, augmented current frame shape descriptors, and augmented previous frame shape descriptors and predicting an affinity matrix using the residual. The residual indicates a spatiotemporal and shape similarity between current detections in a current frame point cloud data and previous detections in a previous frame point cloud data. The affinity matrix indicates associations between the previous detections and the current detections, as well as the augmented anchors.

Abstract: Aspects of the disclosure provide fuel receptacle position/pose estimation for aerial refueling (derived from aircraft position and pose estimation). A video frame, showing an aircraft to be refueled, is received from a single camera. An initial position/pose estimate is determined for the aircraft, which is used to generating an initial rendering of an aircraft model. The video frame and the initial rendering are used to determining refinement parameters (e.g., a translation refinement and a rotational refinement) for the initial position/pose estimate, providing a refined position/pose estimate for the aircraft. The position/pose of a fuel receptacle on the aircraft is determined, based on the refined position/pose estimate for the aircraft, and an aerial refueling boom may be controlled to engage the fuel receptacle. Examples extract features from the aircraft in the video frame and the aircraft model rendering, and use a deep learning neural network (NN) to determine the refinement parameters.