SA-MLP-Mixer: A Compact All-MLP Deep Neural Net Architecture for UAV Navigation in Indoor Environments

Image recognition techniques have become the mainstream solution for indoor unmanned aerial vehicle (UAV) localization and navigation due to the absence of global positioning system. However, unlike autonomous vehicles that enjoy the dividends of the "big model" era, UAVs fail to deploy such models due to the hardware limitations. To this end, this paper proposes a compact all multilayer perceptron (MLP) deep neural network structure that offers a paradigm for theory-guided prompt structural compression of large-scale MLP models. First, we propose a gradient-based sensitivity analysis (GB-SA) method. Unlike existing SA methods, GB-SA obtains nodes' sensitivity indices with gradient information, openning the possibility of efficient SA for large models. We start by integrating GB-SA with MLP and then extend the mode to MLP-Mixer, which is a promising all-MLP deep neural network. By deeply combining GB-SA and MLP-Mixer, SA-MLP-Mixer emerges as a compact model without reducing the model precision. Finally, we evaluate the effectiveness of the proposed model on the benchmark. The experimental results show that SA-MLP-Mixer has an airborne level model scale and accurate localization capability.