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About

Nagasaka Tomomi is a researcher whose work sits at the intersection of robotics, simultaneous localization and mapping (SLAM), and data compression. His primary research focus is on the critical challenge of **map compression**—developing methods to compactly represent large-scale feature maps built by mapper robots. This problem is essential for efficient information sharing in robotic sensor networks and lightweight SLAM applications. Tomomi’s major contribution is pioneering a **dictionary-based approach to compressive SLAM**. He proposed novel incremental schemes that treat map compression as a lossless data compression problem, leveraging dictionary-based techniques to obtain compact representations of sparse feature maps. His work also explores grammar-based compression using Manhattan world priors and modified RANSAC map-matching for improved map alignment. These contributions directly address the Kolmogorov complexity of map data, enabling more efficient storage and transmission. His most cited work, "An incremental scheme for dictionary-based compressive SLAM" (2011), has garnered **21 citations**, demonstrating its foundational impact. Cumulatively, his papers have accumulated over **40 citations**, with notable work appearing in IEEE and Springer venues. Tomomi’s research is particularly valuable for students and engineers working on resource-constrained robotic systems, offering practical solutions for compressing spatial data without sacrificing localization accuracy.