Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation

Thai Viet Dang, Ngoc Tam Bui

研究成果: Article査読


In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.

ジャーナルElectronics (Switzerland)
出版ステータスPublished - 2023 2月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 信号処理
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学


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