S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization

• URL: http://arxiv.org/abs/2205.05861v1
• Date: Thu, 12 May 2022 03:21:45 GMT
• Title: S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization
• Authors: Ran Cheng, Xinyu Jiang, Yuan Chen, Lige Liu, Tao Sun
• Abstract summary: This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN) In the encoding module, a trained S3E network encodes RGB images into embedding codes to implicitly represent spatial and semantic embedding code. With embedding codes and the associated poses obtained from a SLAM system, each image is represented as a graph node in a pose graph. In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization.
• Score: 11.512647893596029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two modules. In the encoding module, a trained S3E network encodes RGB images into embedding codes to implicitly represent spatial and semantic embedding code. With embedding codes and the associated poses obtained from a SLAM system, each image is represented as a graph node in a pose graph. In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization. We collect various scene datasets in the challenging environments to perform experiments. Our results demonstrate that S3E-GNN method outperforms the traditional Bag-of-words (BoW) for camera relocalization due to learning-based embedding and GNN powered scene matching mechanism.

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