Tsubasa Goto

Papers

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About

Tsubasa Goto is a researcher in robotics and computer vision, with a primary focus on indoor localization and perception for mobile service robots. His key contributions center on developing robust localization techniques using spherical cameras, which capture a full 360-degree view of the environment. In his most cited work, "Line-Based Global Localization of a Spherical Camera in Manhattan Worlds" (2018, 17 citations), Goto introduced a novel method that leverages the geometric structure of indoor spaces—specifically Manhattan World assumptions—to achieve reliable global localization. This approach overcomes limitations of conventional cameras by using complete environmental information, enabling robots to determine their position without prior pose estimates. Goto’s research addresses critical challenges in service robotics, such as navigation in cluttered or symmetrical indoor environments. His work has been recognized for its practical applicability in real-world settings, contributing to the advancement of autonomous robot navigation. With a growing citation record, Goto continues to influence the fields of visual localization and robotic perception, offering solutions that enhance the autonomy and reliability of mobile robots in human-centric spaces.