OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments

Yinan Deng, Jiahui Wang, Jingyu Zhao, Xinyu Tian, Guangyan Chen, Yi Yang, Yufeng Yue
Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Robotics (cs.RO)
2024-03-14 00:00:00
Environment maps endowed with sophisticated semantics are pivotal for facilitating seamless interaction between robots and humans, enabling them to effectively carry out various tasks. Open-vocabulary maps, powered by Visual-Language models (VLMs), possess inherent advantages, including multimodal retrieval and open-set classes. However, existing open-vocabulary maps are constrained to closed indoor scenarios and VLM features, thereby diminishing their usability and inference capabilities. Moreover, the absence of topological relationships further complicates the accurate querying of specific instances. In this work, we propose OpenGraph, a representation of open-vocabulary hierarchical graph structure designed for large-scale outdoor environments. OpenGraph initially extracts instances and their captions from visual images using 2D foundation models, encoding the captions with features to enhance textual reasoning. Subsequently, 3D incremental panoramic mapping with feature embedding is achieved by projecting images onto LiDAR point clouds. Finally, the environment is segmented based on lane graph connectivity to construct a hierarchical graph. Validation results from real public dataset SemanticKITTI demonstrate that, even without fine-tuning the models, OpenGraph exhibits the ability to generalize to novel semantic classes and achieve the highest segmentation and query accuracy. The source code of OpenGraph is publicly available at
PDF: OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments.pdf
Empowered by ChatGPT