Sequentially-trained, Shallow Neural Networks for Real-time 3D Odometry

Fourier-domain correlation approaches have been successful in a variety of image comparison approaches but fail when the scenes, patterns, or objects in the images are distorted. Here, we utilize the sequential training of shallow neural networks on Fourier-preprocessed video to infer 3-D movement. The bio-inspired pipeline learns x, y, and z-direction movement from high-frame-rate, low-resolution, Fourier-domain preprocessed inputs (either cross power spectra or phase correlation data). Our pipeline leverages the high sensitivity of Fourier methods in a manner that is resilient to the parallax distortion of a forward-facing camera. Via sequential training over several path trajectories, models generalize to predict the 3-D movement in unseen trajectory environments. Models with no hidden layer are less accurate initially but converge faster with sequential training over different flightpaths. Our results show important considerations and trade-offs between input data preprocessing (compression) and model complexity (convergence).