Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots

TL;DR

This paper introduces a deep learning method that combines RGB-D data and 3D clustering to enhance material recognition and semantic mapping in mobile robots, improving accuracy over existing approaches.

Contribution

It presents a novel object-oriented, complementarity-aware deep learning approach integrated with SLAM for improved 3D semantic mapping and material classification.

Findings

Significant improvement in material classification accuracy

Enhanced 3D clustering precision in semantic maps

Validated on public and real-world robot datasets

Abstract

Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles. RGB-based scene-level semantic segmentation has been well-addressed in the literature. However, improving material recognition using the depth modality and its integration with SLAM algorithms for 3D semantic mapping could unlock new potential benefits in the robotics perception pipeline. To this end, we propose a complementarity-aware deep learning approach for RGB-D-based material classification built on top of an object-oriented pipeline. The approach further integrates the ORB-SLAM2 method for 3D scene mapping with multiscale clustering of the detected material semantics in the point cloud map generated by the visual SLAM algorithm. Extensive experimental results with existing public datasets and newly contributed real-world robot datasets demonstrate a significant improvement in material classification and 3D clustering accuracy compared to state-of-the-art approaches for 3D semantic scene mapping.