Kanako Kinoshita

Papers

Total Citations

H-Index

About

Kanako Kinoshita is a researcher in robotics and computer vision, with a focused expertise in obstacle detection for autonomous systems. Her work addresses a critical challenge in the field of unmanned transport robots: the need for flexible, real-time hazard perception beyond pre-programmed routes. Kinoshita’s major contribution lies in developing a novel method that integrates a standard camera with a line laser to detect obstacles in a robot’s path. This approach, detailed in her two most-cited papers from 2019—*"Proposal of a Method for Obstacle Detection by the Use of Camera and Line Laser"* and *"A Method for Detection of Obstacle Using Line Laser and Camera"*—offers a low-cost, efficient solution for collision avoidance. By projecting a structured laser line and analyzing its deformation via camera imagery, her technique enables robots to identify unexpected packages or objects, enhancing safety and autonomy. While each paper has garnered 2 citations, their impact is notable for laying foundational work in practical, sensor-fusion-based navigation. Kinoshita’s research is particularly valuable for advancing logistics and warehouse automation, where reliable obstacle detection is key to enabling dynamic route adjustment. Her contributions underscore a pragmatic approach to making unmanned systems more adaptable and collision-resistant in real-world environments.