Jared Paquet
Papers
1
Total Citations
4
H-Index
1
About
Jared Paquet is a researcher focused on robotics, computer vision, and autonomous localization, with a particular emphasis on distributed sensing and deep learning. His most cited work, "A Distributed Sensing Approach for Single Platform Image-Based Localization" (2018, 4 citations), introduces a novel system that uses four non-stereo monocular cameras and a deep convolutional network—a modified version of the PoseNet architecture—to regress the position of a ground robot. This contribution advances the field of image-based robot (re)localization by demonstrating how multiple cameras can be leveraged for robust single-platform positioning without stereo depth. While his citation count is modest, Paquet’s work is notable for its practical integration of distributed sensing with modern deep learning, offering a scalable solution for autonomous navigation in constrained environments. His research bridges the gap between classical localization techniques and contemporary neural network approaches, making it relevant for students and researchers exploring low-cost, vision-based robotics.
Research Focus
Key Achievements
Top Papers
- 1