Towards a unified multiresolution vision model for autonomous ground robots

While remotely operated unmanned vehicles are increasingly a part of everyday life, truly autonomous robots capable of independent operation in dynamic environments have yet to be realized-particularly in the case of ground robots required to interact with humans and their environment. We present a unified multiresolution vision model for this application designed to provide the wide field of view required to maintain situational awareness and sufficient visual acuity to recognize elements of the environment while permitting feasible implementations in real-time vision applications. The model features a kind of color-constant processing through single-opponent color channels and contrast invariant oriented edge detection using a novel implementation of the Combination of Receptive Fields model. The model provides color and edge-based salience assessment, as well as a compressed color image representation suitable for subsequent object identification. We show that bottom-up visual saliency computed using this model is competitive with the current state-of-the-art while allowing computation in a compressed domain and mimicking the human visual system with nearly half (45%) of computational effort focused within the fovea. This method reduces storage requirement of the image pyramid to less than 5% of the full image, and computation in this domain reduces model complexity in terms of both computational costs and memory requirements accordingly. We also quantitatively evaluate the model for its application domain by using it with a camera/lens system with a 185°field of view capturing 3.5M pixel color images by using a tuned salience model to predict human fixations.