Place Recognition CNN With Trainable Feature Compression

The patent extracts only the most discriminative and informative features from the image data through the CNN model, rather than processing all raw pixel information. The feature extraction process selectively identifies and retains key visual characteristics that are most relevant for place recognition, discarding redundant information that would increase computational burden without contributing to recognition accuracy.

The patent combines multiple feature extraction operations into a unified CNN model that performs feature extraction, selection, and aggregation in an integrated end-to-end framework. This merging of operations allows the system to achieve high-dimensional feature representation while optimizing computational efficiency through shared weights and parameter reuse across different processing stages.

The patent segments the training process into distinct but interconnected components: feature extraction layers, feature aggregation layers, and loss computation layers. Each segment can be independently configured and optimized, allowing researchers to control training complexity by adjusting individual components while maintaining the benefits of end-to-end training through gradient flow across all segments.

The patent introduces intermediate feature representations and loss functions that act as mediators between the input images and final recognition decisions. These intermediates provide structured pathways for gradient propagation during backpropagation, simplifying the training dynamics while enabling end-to-end optimization. The intermediate representations serve as natural checkpoints that facilitate controlled training complexity.