Practical Implementation of Visual Navigation Based on Semantic Segmentation for Human-Centric Environments
Miho Adachi, Kazufumi Honda, Junfeng Xue, H. Sudo, Yuriko Ueda, Yuki Yuda, Marin Wada, Ryusuke Miyamoto
Abstract
This study focuses on visual navigation methods for autonomous mobile robots based on semantic segmentation results. The challenge is to perform the expected actions without being affected by the presence of pedestrians. Therefore, we implemented a semantics-based localization method that is not affected by dynamic obstacles and a direction change method at intersections that functions even with coarse-grain localization results. The proposed method was evaluated through driving experiments in the Tsukuba Challenge 2022, where a 290 m run including 10 intersections was achieved in the confirmation run section.
Topics & Concepts
Computer scienceSegmentationSemantics (computer science)Computer visionArtificial intelligenceMobile robotRobotHuman–computer interactionProgramming languageRobotics and Sensor-Based LocalizationRobotic Path Planning AlgorithmsRobotics and Automated Systems