Eduardo Molinos

About

Eduardo Molinos is a robotics researcher whose work sits at the intersection of autonomous navigation, simultaneous localization and mapping (SLAM), and dynamic obstacle avoidance — areas critical to the next generation of intelligent mobile systems. His research has made particularly significant contributions to enabling Micro Aerial Vehicles (MAVs) to operate reliably in GPS-denied and indoor environments, where precise self-localization is notoriously difficult. His multi-sensorial SLAM system for low-cost MAVs (2017, 78 citations) and his earlier work fusing monocular cameras with laser sensors (2015–2016) established robust frameworks that researchers continue to build upon today. Molinos has also shaped how the robotics community understands dynamic environments. His review of Detection and Tracking of Moving Obstacles (DATMO, 2019, 51 citations) serves as a valuable reference for practitioners, while his dynamic window and Bayesian occupancy filter approaches offer practical algorithmic solutions for real-world obstacle avoidance. His WiFi-based SLAM comparisons further demonstrate a pragmatic interest in low-cost, deployable solutions. With over 370 cumulative citations across his published work, Molinos represents an important voice in making autonomous robots safer, smarter, and more accessible in complex, unstructured environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Dynamic window based approaches for avoiding obstacles in moving

   90 citations · 2019
2. 2
   A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

   78 citations · 2017
3. 3
   Indoor SLAM for Micro Aerial Vehicles Control Using Monocular Camera and Sensor Fusion

   54 citations · 2016
4. 4
   Detection and Tracking of Moving Obstacles (DATMO): A Review

   51 citations · 2019
5. 5
   Indoor SLAM for Micro Aerial Vehicles Using Visual and Laser Sensor Fusion

   23 citations · 2015
6. 6
   Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference

   22 citations · 2013
7. 7
   A comparison of SLAM algorithms with range only sensors

   19 citations · 2014
8. 8
   WiFi SLAM algorithms: an experimental comparison

   17 citations · 2014
9. 9
   Dynamic obstacle avoidance based on curvature arcs

   13 citations · 2014
10. 10
    Dynamic process migration in heterogeneous ROS-based environments

    7 citations · 2015

Key Collaborators

Contact & Links