Areolino de Almeida Neto
Papers
5
Total Citations
15
H-Index
3
About
Areolino de Almeida Neto is a robotics researcher whose work centers on bio-inspired simultaneous localization and mapping (SLAM) and autonomous navigation in dynamic environments. His primary contributions lie in advancing RatSLAM—a computational model based on rodent brain navigation—by making it more extensible, efficient, and suitable for real-world deployment. Notably, his xRatSLAM framework (2022) provides a modular architecture for researchers to build upon, while his parallel C++ library implementation (2019) significantly improves computational performance. Neto also addresses practical challenges in mobile robotics, including sensor data fusion for indoor mapping using low-cost sensors (2013, 5 citations) and hierarchical obstacle avoidance in dynamic settings (2016). His multisession SLAM approach (2023) tackles the critical problem of incremental map building across multiple robot runs, a key requirement for long-term autonomous navigation. Though his citation counts are modest, reflecting the specialized nature of his work, Neto’s contributions provide foundational tools and algorithms that enable other researchers to experiment with and extend RatSLAM-based systems, bridging the gap between biological inspiration and practical robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Multisession SLAM Approach for RatSLAM3 citations · 2023
- 3xRatSLAM: An Extensible RatSLAM Computational Framework3 citations · 2022
- 4A Parallel RatSlam C++ Library Implementation2 citations · 2019
- 5Obstacle Avoidance in Dynamic Environment: a Hierarchical Solution2 citations · 2016