# Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots Source: [Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:00:03.231Z Authors: Siva Krishna Ravipati, Ehsan Latif, Ramviyas Parasuraman, Suchendra M. Bhandarkar Published: 2024-07-08T16:25:01.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()