A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications

Understand

Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely.

• However, the lack of coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces places the burden of traversability estimation heavily on the mobile robot.
• This paper explores the use of a self-supervised one-shot texture segmentation framework and an RGB-D camera to achieve robust drivable area segmentation.
• With a fast inference speed and compact size, the developed model, MOSTS is ideal for real-time robot navigation and various embedded applications.