Acceleration of real time computer vision processing on UAVs through GPS attitude estimation

A non-transitory article of manufacture tangibly embodying computer readable instructions, which when implemented, cause a computer to perform the steps of a method for applying GPS UAV attitude estimation to image analysis, the UAV having a plurality of GPS receivers mounted at fixed locations on the UAV and a camera, comprising;

• receiving GPS signals from each GPS satellite in view of the UAV, the GPS signals comprising respective pn codes and carrier frequencies;
• determining carrier phase and pseudo-range measurements for each GPS receiver fixed to the UAV based on the respective pn codes and carrier frequencies, the pseudo-range and carrier phase data measurements representing the distance between each GPS receiver fixed to the UAV and each GPS satellite in view of the UAV;
• comparing the GPS carrier phase and pseudo-range measurements pair-wise for each pair of GPS receivers fixed to the UAV and GPS satellite in view of the UAV to determine relative distance measurements;
• determining an attitude of the UAV based on the relative distance measurements at each of a plurality of time points;
• determining a 3D camera pose rotation matrix based on the attitude of the UAV comprising the steps of:
• determining a first attitude of the UAV at a first time point;
• determining a second attitude of the UAV at a second time point;
• encoding the first attitude of the UAV as a first 3×3 rotation matrix and encoding the second attitude of the UAV as a second 3×3 rotation matrix;
• calculating a rotation of the camera between the first and second time points by multiplying the first rotation matrix by the inverse of the second rotation matrix and encoding the results as the 3D camera pose rotation matrix;
• and
• estimating the camera pose rotation between the first time point and the second time point;
• receiving image data from a camera mounted on the UAV taken at the first time point and the second time point;
• and
• analyzing the image data received from the UAV at the first and second time points using the estimated camera pose rotation to predict coordinates of an object in the image data at the second time point.

A computer system for applying GPS UAV attitude estimation to image analysis, the UAV having a plurality of GPS receivers mounted at fixed locations on the UAV and a camera, comprising:

• one or more computer processors;
• one or more non-transitory computer-readable storage media;
• program instructions, stored on the one or more non-transitory computer-readable storage media, which when implemented by the one or more processors, cause the computer system to perform the steps of:
• receiving GPS signals from each GPS satellite in view of the UAV, the GPS signals comprising respective pn codes and carrier frequencies;
• determining carrier phase and pseudo-range measurements for each GPS receiver fixed to the UAV based on the respective pn codes and carrier frequencies, the pseudo-range and carrier phase data measurements representing the distance between each GPS receiver fixed to the UAV and each GPS satellite in view of the UAV;
• comparing the GPS carrier phase and pseudo-range measurements pair-wise for each pair of GPS receivers fixed to the UAV and GPS satellite in view of the UAV to determine relative distance measurements;
• determining an attitude of the UAV based on the relative distance measurements at each of a plurality of time points;
• determining a 3D camera pose rotation matrix based on the attitude of the UAV comprising the steps of:
• determining a first attitude of the UAV at a first time point;
• determining a second attitude of the UAV at a second time point;
• encoding the first attitude of the UAV as a first 3×3 rotation matrix and encoding the second attitude of the UAV as a second 3×3 rotation matrix;
• calculating a rotation of the camera between the first and second time points by multiplying the first rotation matrix by the inverse of the second rotation matrix and encoding the results as the 3D camera pose rotation matrix;
• and
• estimating the camera pose rotation between the first time point and the second time point;
• receiving image data from a camera mounted on the UAV taken at the first time point and the second time point;
• and
• analyzing the image data received from the UAV at the first and second time points using the estimated camera pose rotation to predict coordinates of an object in the image data at the second time point.