Papers
9
Total Citations
192
H-Index
6
About
J.C. del Toro is a leading researcher in mobile robotics, specializing in autonomous navigation, simultaneous localization and mapping (SLAM), and human-robot interaction. His most influential work centers on developing novel, curvature-based methods for natural landmark extraction from 2D laser rangefinder data—a foundational contribution that has shaped how robots perceive and navigate unstructured environments. His landmark 2006 paper on feature extraction from laser scan data (53 citations) introduced an efficient, three-module system for geometric feature detection, while his 2007 work on adaptive curvature estimation (55 citations) advanced the robust identification of natural landmarks. Del Toro’s 2010 multi-criteria optimization strategy for shared control in wheelchair navigation (57 citations) demonstrates his commitment to assistive robotics, blending autonomy with user intent. His research also explores hybrid navigation architectures and data-driven attention mechanisms for visual landmark acquisition, bridging model-based and learning approaches. With over 190 total citations, del Toro’s work remains essential reading for researchers in field robotics, SLAM, and human-robot collaboration, offering practical algorithms that continue to underpin modern autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 5Hybrid navigation guidance for intelligent mobiles6 citations · 2006
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- 8RETARGETING SYSTEM FOR A SOCIAL ROBOT IMITATION INTERFACE2 citations · 2008
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