Effective method to estimate pose, velocity and attitude with uncertainty

A computer system for utilizing an uncertainty map to select a best set of optical flow estimates for one or more vehicles, the computer system comprising:

• a memory having processor-readable instructions stored therein;
• and
• one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for:
• receiving one or more images captured by one or more cameras of at least one vehicle;
• analyzing the one or more images by inputting the one or more images into a trained machine-learning model, wherein the trained machine-learning model is configured to process the one or more images to determine an optical flow map and an uncertainty map, the optical flow map indicating a motion of one or more objects in the one or more images, the uncertainty map including one or more uncertainty values indicating a reliability of the optical flow map;
• determining a feature map based on the optical flow map;
• determining an improved feature map by applying the uncertainty map to the feature map;
• transforming the improved feature map by applying at least one transformation or optimization to the improved feature map;
• and
• based on the transformed improved feature map, estimating a pose value, a velocity value, or an attitude value.

A non-transitory computer-readable medium containing instructions for utilizing an uncertainty map to select a best set of optical flow estimates for one or more vehicles, the instructions comprising:

• receiving one or more images captured by one or more cameras of at least one vehicle;
• analyzing the one or more images by inputting the one or more images into a trained machine-learning model, wherein the trained machine-learning model is configured to process the one or more images to determine an optical flow map and an uncertainty map, the optical flow map indicating a motion of one or more objects in the one or more images, the uncertainty map including one or more uncertainty values indicating a reliability of the optical flow map;
• determining a feature map based on the optical flow map;
• determining an improved feature map by applying the uncertainty map to the feature map;
• transforming the improved feature map by applying at least one transformation or optimization to the improved feature map;
• and
• based on the transformed improved feature map, estimating a pose value, a velocity value, or an attitude value.