Andres Gonzalez Moreno
Papers
1
Total Citations
3
H-Index
1
About
Andres Gonzalez Moreno is a researcher advancing the state of the art in dynamic environment perception for robotics and autonomous systems. His primary focus lies in developing sophisticated probabilistic models for spatial occupancy estimation, particularly through dynamic occupancy grid filtering. His most-cited work, "A cross-prediction, hidden-state-augmented approach for Dynamic Occupancy Grid filtering" (2022), introduces a novel methodology that enhances the accuracy of modeling complex, moving environments by augmenting hidden states and leveraging cross-prediction techniques. This contribution addresses a critical gap in robotics and automotive applications, moving beyond traditional static grid-mapping to enable sub-object-level tracking of dynamic scenes. With 3 citations, this paper is gaining recognition for its potential to improve real-time decision-making in autonomous vehicles and mobile robots. Gonzalez Moreno’s work is foundational for researchers seeking robust, scalable solutions to the challenge of navigating unpredictable, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1