Daniel Casado Herraez

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About

Daniel Casado Herraez is a rising researcher at the intersection of autonomous robotics and perception, whose work is critical for enabling robots and vehicles to safely navigate dynamic environments. His primary research areas center on radar-based perception, semantic mapping, and task planning for mobile robots. Herraez’s major contribution is the development of "Radar Tracker," a novel framework for moving instance tracking in sparse and noisy radar point clouds. This work directly addresses the challenge of collision avoidance and reliable path planning for autonomous vehicles, demonstrating how robots can maintain situational awareness even with imperfect sensor data. With 4 citations since its 2024 publication, this paper is already establishing a foundation for robust radar perception. Complementing this, Herraez has also advanced onboard semantic mapping for action graph estimation, enabling robots to understand their surroundings and identify interactable objects for task planning. His research bridges low-level sensor processing with high-level reasoning, offering a comprehensive approach to autonomy. As a researcher focused on turning noisy radar data into actionable intelligence, Herraez is contributing essential building blocks for the next generation of self-aware, decision-making robots.