Semantic Exploration and Dense Mapping for Autonomous Mobile Robots

This research addresses critical challenges in autonomous robot exploration by developing novel algorithms that integrate semantic understanding with exploration strategies to build comprehensive semantic maps while efficiently exploring unknown environments. The technical approach combines advanced semantic SLAM frameworks featuring real-time semantic segmentation, 3D semantic reconstruction from RGB-D sensor data, loop closure detection using semantic landmarks, and global semantic map optimization with intelligent exploration planning including semantic-aware frontier detection, information-theoretic exploration objectives, and multi-scale planning from local to global scales. Key innovations include voxel-based semantic occupancy mapping with temporal consistency enforcement, deep learning for real-time semantic segmentation, 3D object detection and instance segmentation, scene graph construction for spatial relationships, and uncertainty quantification for semantic predictions that enable mobile robots to maximize information gain while minimizing exploration time and creating detailed geometric and semantic representations.

The developed framework demonstrates significant impact across service robotics and emergency response applications, with successful deployment in indoor navigation and assistance robots, cleaning and maintenance automation, security and surveillance systems, healthcare facility monitoring, disaster area mapping, and hazardous environment assessment. Real-world validation includes field testing in indoor office and residential environments, outdoor urban and campus settings, mixed indoor-outdoor scenarios, and multi-floor building exploration with quantitative results showing improved semantic segmentation accuracy, geometric reconstruction precision, coverage rate optimization, and path efficiency compared to baseline exploration strategies. The research provides joint optimization of exploration and semantic mapping, uncertainty-aware semantic exploration objectives, hierarchical semantic map representations, and efficient incremental semantic map updates with real-time performance on mobile robot platforms. The team’s expertise in semantic SLAM, autonomous exploration, and multi-robot systems positions them to advance next-generation capabilities including foundation models for robust semantic understanding, distributed semantic mapping with robot teams, and communication-efficient protocols for collaborative exploration and consensus building.

Acknowledgments

This work was supported by research grants and robotics industry collaborations. We thank the computer vision and robotics communities for providing datasets and evaluation frameworks.

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For complete technical details and experimental results, please refer to the original publication: 25-ral-xiaoyang-zhan-semantic-exploration-and-dense-mapping.pdf