Candy Espulgar
Papers
1
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1
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About
Candy Espulgar is a robotics researcher whose work centers on advancing Simultaneous Localization and Mapping (SLAM) through acoustic sensing, a critical area for autonomous mobile robots. Her major contribution lies in the development and performance analysis of an orthogonal sonar array designed specifically for SLAM implementation, offering a compelling alternative to traditional visual and LiDAR-based systems. This acoustic SLAM (aSLAM) approach addresses key limitations of visual SLAM, such as poor lighting and texture dependency, while providing a more cost-effective solution than LiDAR. Her research, detailed in her 2024 paper "Development and Performance Analysis of Orthogonal Sonar Array for Autonomous Mobile Robot SLAM Implementation," has already garnered attention within the field. By pioneering efficient aSLAM techniques, Espulgar is helping to make autonomous navigation more accessible and robust, particularly for environments where conventional sensors struggle. Her work represents a significant step toward practical, affordable, and reliable robotic perception systems.
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