Matheus Chaves Menezes

About

Matheus Chaves Menezes is a robotics researcher whose work focuses on bio-inspired simultaneous localization and mapping (SLAM), particularly through the lens of RatSLAM—a navigation algorithm modeled after rodent brain function. His major contributions center on making RatSLAM more practical, efficient, and scalable for real-world robotic applications. Menezes developed an automatic tuning method for RatSLAM’s parameters using Irace and Iterative Closest Point (8 citations), significantly reducing the manual calibration burden across different environments. He also pioneered a neuro-inspired multi-robot approach that leverages shared video information to accelerate map creation (6 citations), addressing a key bottleneck in multi-agent systems. His work on multisession SLAM (3 citations) enables incremental map building over multiple runs, while the xRatSLAM framework (3 citations) provides an extensible computational platform for future research. Additionally, Menezes implemented a parallel C++ library for RatSLAM (2 citations), improving computational efficiency. His research bridges neuroscience and robotics, offering practical solutions for autonomous navigation in complex, real-world environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Automatic Tuning of RatSLAM’s Parameters by Irace and Iterative Closest Point

   8 citations · 2020
2. 2
   A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems

   6 citations · 2018
3. 3
   A Multisession SLAM Approach for RatSLAM

   3 citations · 2023
4. 4
   xRatSLAM: An Extensible RatSLAM Computational Framework

   3 citations · 2022
5. 5
   A Parallel RatSlam C++ Library Implementation

   2 citations · 2019

Key Collaborators

Contact & Links