Sequentially trained, shallow neural networks for real-time 3D odometry

Frank Rodriguez; Baurzhan Muminov; Luat T. Vuong

doi:10.1117/12.3005250

17 October 2023 Sequentially trained, shallow neural networks for real-time 3D odometry

Frank Rodriguez, Baurzhan Muminov, Luat T. Vuong

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 127420X (2023) https://doi.org/10.1117/12.3005250
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

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).

Citation Download Citation

Frank Rodriguez, Baurzhan Muminov, and Luat T. Vuong "Sequentially trained, shallow neural networks for real-time 3D odometry", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 127420X (17 October 2023); https://doi.org/10.1117/12.3005250

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