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

Thai Viet Dang, Ngoc Tam Bui

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number533
JournalElectronics (Switzerland)
Issue number3
Publication statusPublished - 2023 Feb


  • computer vision
  • fully convolutional networks
  • mobile robot
  • navigation
  • obstacle avoidance
  • semantic segmentation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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