Environmental Mapping for Rat Robots Using Hierarchical ORB-SLAM

Yuhong Huang, Han Li, Boyang Li, Zitao Zhang, Zhenshan Bing, Kai Huang, Alois Knoll

Keywords: Rat robot, SLAM, Low-texture environments, Hierarchical framework

TL;DR: This paper proposes a hierarchical SLAM framework that runs monocular ORB-SLAM remotely and combines coarse and fine matching strategies to enable stable mapping for rat robots in low-texture environments.

Abstract:

Simultaneous Localization and Mapping (SLAM) on small quadruped robots, such as rat robots, is challenged by body vibrations, limited onboard computation, and low-texture environments. These factors cause sparse features, unstable viewpoints, and frame loss, which hinder conventional SLAM.
To address these challenges, we redesign the rat robot with an external antenna to improve communication and a monocular camera to reduce bandwidth, while running monocular ORB-SLAM on a remote computer. In addition, we introduce a hierarchical SLAM framework that switches between coarse frame matching under sparse features and fine keyframe matching when features are sufficient.
Experiments in a low-texture drainage channel show that our system maintains continuous mapping despite frequent interruptions, demonstrating feasibility for real-world deployment of rat-sized quadruped robots.

Submission Type: Novel research

Student Paper: Yes

Demo Or Video: Yes

Public Extended Abstract: Yes

Submission Number: 8