Papers
7
Total Citations
65
H-Index
4
About
Arcangelo Bruna is a researcher at the forefront of computer vision and robotics, specializing in the challenges posed by non-standard imaging sensors and embedded intelligence. His foundational work addresses the critical problem of feature detection and gradient estimation in images from wide-angle and fisheye cameras—sensors essential for automotive, surveillance, and robotics applications. His paper "Affine Covariant Features for Fisheye Distortion Local Modeling" (27 citations) provides a robust method for extracting reliable visual features from heavily distorted images, while "Generalized Sobel Filters for gradient estimation of distorted images" (14 citations) offers a principled solution for accurate gradient computation in such challenging conditions. More recently, Bruna has pivoted toward deep learning for real-world robotic systems, developing methods for localization using 2D laser scanners and efficient voice command recognition on resource-constrained microcontrollers. His work on ORB-SLAM with near-infrared images and low-cost point-to-point navigation demonstrates a commitment to practical, deployable solutions. Notably, his 2023 study on an embedded EOG-based brain-computer interface for robotic control highlights an innovative foray into assistive technology, aiming to empower individuals with motor disabilities. With a growing citation record, Bruna’s research bridges the gap between robust computer vision algorithms and their efficient implementation on embedded platforms.
Research Focus
Key Achievements
Top Papers
- 1Affine Covariant Features for Fisheye Distortion Local Modeling27 citations · 2016
- 2Generalized Sobel Filters for gradient estimation of distorted images14 citations · 2015
- 3Deep Learning Localization with 2D Range Scanner10 citations · 2021
- 4
- 5ORB-SLAM with Near-infrared images and Optical Flow data4 citations · 2021
- 6Low Cost Point to Point Navigation System3 citations · 2021
- 7