Garbage Segmentation and Attribute Analysis by Robotic Dogs

TL;DR

This paper introduces GSA2Seg, a novel visual system using robotic dogs for waste detection, segmentation, and attribute analysis to improve garbage collection in various environments.

Contribution

The study presents GSA2Seg, integrating advanced visual perception and open-vocabulary attribute analysis, along with a new dataset for evaluating robotic waste management capabilities.

Findings

Robotic dogs effectively identify and segment garbage in diverse environments.

GSA2Seg improves garbage grasping accuracy through attribute analysis.

Extensive experiments demonstrate the system's robustness and effectiveness.

Abstract

Efficient waste management and recycling heavily rely on garbage exploration and identification. In this study, we propose GSA2Seg (Garbage Segmentation and Attribute Analysis), a novel visual approach that utilizes quadruped robotic dogs as autonomous agents to address waste management and recycling challenges in diverse indoor and outdoor environments. Equipped with advanced visual perception system, including visual sensors and instance segmentators, the robotic dogs adeptly navigate their surroundings, diligently searching for common garbage items. Inspired by open-vocabulary algorithms, we introduce an innovative method for object attribute analysis. By combining garbage segmentation and attribute analysis techniques, the robotic dogs accurately determine the state of the trash, including its position and placement properties. This information enhances the robotic arm's grasping capabilities, facilitating successful garbage retrieval. Additionally, we contribute an image dataset, named GSA2D, to support evaluation. Through extensive experiments on GSA2D, this paper provides a comprehensive analysis of GSA2Seg's effectiveness. Dataset available: \href{https://www.kaggle.com/datasets/hellob/gsa2d-2024}{https://www.kaggle.com/datasets/hellob/gsa2d-2024}.