no code implementations • 27 Oct 2022 • Priyesh Shukla, Sureshkumar S., Alex C. Stutts, Sathya Ravi, Theja Tulabandhula, Amit R. Trivedi
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning.
no code implementations • 13 Nov 2021 • Priyesh Shukla, Shamma Nasrin, Nastaran Darabi, Wilfred Gomes, Amit Ranjan Trivedi
Using Bayesian inference, not only the prediction itself, but the prediction confidence can also be extracted for planning risk-aware actions.
no code implementations • 16 Feb 2021 • Priyesh Shukla, Ankith Muralidhar, Nick Iliev, Theja Tulabandhula, Sawyer B. Fuller, Amit Ranjan Trivedi
Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3D map representation using a harmonic mean of "Gaussian-like" mixture (HMGM) model.
Indoor Localization Robotics Hardware Architecture Image and Video Processing B.7; I.2.9
no code implementations • 28 Feb 2020 • Priyesh Shukla, Ahish Shylendra, Theja Tulabandhula, Amit Ranjan Trivedi
This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM.