Optical measurement system to land an aerial vehicle

A method for landing an aerial vehicle on a platform, the method comprising:

• receiving, by one or more processors via a camera coupled to the aerial vehicle, a plurality of image frames captured by the camera of the platform on which to land the aerial vehicle;
• generating, by the one or more processors via a first model trained with machine learning on reference patterns of images, a feature map that identifies a predetermined pattern in an image frame of the plurality of image frames that corresponds to a zone on the platform on which to land the aerial vehicle;
• inputting, by the one or more processors, responsive to recognition of the predetermined pattern from the image frame, the feature map generated by the first model into a second model trained with machine learning on slope-intercept functions according to an angular offset between features of the predetermined pattern, to output an orientation of the aerial vehicle relative to the zone;
• determining, by the one or more processors, based on an offset of the zone in the image frame and the orientation output by the second model fed from the first model of the aerial vehicle relative to the zone, a vector between the aerial vehicle and the zone;
• and
• providing for display, by the one or more processors via a display device communicatively coupled to a computing system, an indication of the vector overlayed on a digital representation of the image frame.

An aerial vehicle, comprising:

• a computing system comprising one or more processors, coupled with memory, to:
• receive, via a camera coupled to the aerial vehicle, a plurality of image frames captured by the camera of a platform on which to land the aerial vehicle;
• generate, via a first model trained with machine learning on reference patterns of images, a feature map that identifies a predetermined pattern in an image frame of the plurality of image frames that corresponds to a zone on the platform on which to land the aerial vehicle;
• input, responsive to recognition of the predetermined pattern from the image frame, the feature map generated by the first model into a second model trained with machine learning on slope-intercept functions according to an angular offset between features of the predetermined pattern, to output an orientation of the aerial vehicle relative to the zone;
• determine, based on an offset of the zone in the image frame and the orientation output by the second model fed from the first model of the aerial vehicle relative to the zone, a vector between the aerial vehicle and the zone;
• and
• provide, for display via a display device communicatively coupled to the computing system, an indication of the vector overlayed on a digital representation of the image frame.