Search Results for author: Valdir Grassi Jr

Found 8 papers, 3 papers with code

Autonomous driving of trucks in off-road environment

no code implementations12 Dec 2023 Kenny A. Q. Caldas, Filipe M. Barbosa, Junior A. R. Silva, Tiago C. Santos, Iago P. Gomes, Luis A. Rosero, Denis F. Wolf, Valdir Grassi Jr

The longitudinal control strategy relies on a Non-Linear Model Predictive Controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints.

Autonomous Driving

On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation

no code implementations13 Oct 2020 Raul de Queiroz Mendes, Eduardo Godinho Ribeiro, Nicolas dos Santos Rosa, Valdir Grassi Jr

Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes.

 Ranked #1 on Surface Normals Estimation on NYU-Depth V2 Surface Normals (using extra training data)

Autonomous Navigation Depth Completion +4

Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

1 code implementation24 Sep 2018 Nícolas Rosa, Vitor Guizilini, Valdir Grassi Jr

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions.

Monocular Depth Estimation

Robust path-following control for articulated heavy-duty vehicles

no code implementations7 Aug 2018 Filipe Marques Barbosa, Lucas Barbosa Marcos, Maira Martins da Silva, Marco Henrique Terra, Valdir Grassi Jr

Parametric uncertainties were assumed to be on the payload, and an $\mathcal{H}_{\infty}$ controller was used for performance comparison.

Systems and Control Systems and Control 37N35(Primary), 62G35, 70Q05, 93C85, 70E60 (Secondary)

Learning to Race through Coordinate Descent Bayesian Optimisation

no code implementations17 Feb 2018 Rafael Oliveira, Fernando H. M. Rocha, Lionel Ott, Vitor Guizilini, Fabio Ramos, Valdir Grassi Jr

On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system.

Bayesian Optimisation Car Racing +1

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