Emi Matsumoto

Papers

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About

Dr. Emi Matsumoto is a robotics researcher whose work focuses on autonomous mobile robot navigation and perception in human-centered environments. Her most significant contribution, "Data Squashing for HSV Subimages by an Autonomous Mobile Robot" (2012), introduced a novel data compression technique for color-based visual processing, enabling efficient real-time image analysis on resource-constrained platforms. This paper, with 6 citations, demonstrates her early work in optimizing sensor data for robotic systems. In her complementary study, "Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office" (2011), she applied support vector machines to human detection and collision avoidance, addressing a critical challenge in social robotics. Though modest in citation count, these works represent foundational steps in integrating machine learning with low-cost hardware for safe human-robot interaction. Dr. Matsumoto’s research bridges computer vision and autonomous navigation, contributing to the development of smaller, smarter robots capable of operating in dynamic, crowded spaces. Her work remains relevant for researchers exploring efficient perception systems for mobile robotics.