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

Autor:	Ravipati, Siva Krishna, Latif, Ehsan, Parasuraman, Ramviyas, Bhandarkar, Suchendra M.
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to IROS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.06077 Zobrazit plný text záznamu View this record from Arxiv