A computer-implemented method for identifying a pose in a first image comprising: establishing an initial database of image triplets; in a pose estimation processor: training a deep learning neural network using the initial database of image triplets; calculating the pose for the first image using the deep learning neural network; comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network; creating a new set of training data (113, 115) including a plurality of error cases identified in a plurality of input images; retraining the deep learning neural network using the new set of training data (113, 115), and the method characterized in the pose estimation processor conducting statistical analysis on a plurality of error cases to select a subset of the plurality of error cases to be included in the new set of training data (113, 115), wherein the statistical analysis comprises at least one of: identifying a cluster center of error cases located within a predetermined radius within an input image, or identifying a degree of error in an error case and selecting a first error case having a larger degree of error than a second error case having a smaller degree of error than the first error case, and including the first error case in the new set of training data (113, 115), or identifying a first error case having a greater number of neighbor regions of the image identified as error cases than a second error case having a fewer number of neighbor region of the image identified as error cases. training a deep learning neural network using the initial database of image triplets; calculating the pose for the first image using the deep learning neural network; comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network; creating a new set of training data (113, 115) including a plurality of error cases identified in a plurality of input images; retraining the deep learning neural network using the new set of training data (113, 115), and the method characterized in the pose estimation processor conducting statistical analysis on a plurality of error cases to select a subset of the plurality of error cases to be included in the new set of training data (113, 115), wherein the statistical analysis comprises at least one of: identifying a cluster center of error cases located within a predetermined radius within an input image, or identifying a degree of error in an error case and selecting a first error case having a larger degree of error than a second error case having a smaller degree of error than the first error case, and including the first error case in the new set of training data (113, 115), or identifying a first error case having a greater number of neighbor regions of the image identified as error cases than a second error case having a fewer number of neighbor region of the image identified as error cases. identifying a cluster center of error cases located within a predetermined radius within an input image, or identifying a degree of error in an error case and selecting a first error case having a larger degree of error than a second error case having a smaller degree of error than the first error case, and including the first error case in the new set of training data (113, 115), or identifying a first error case having a greater number of neighbor regions of the image identified as error cases than a second error case having a fewer number of neighbor region of the image identified as error cases.