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- W4287605890 abstract "We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large scale indoor reconstruction. To achieve this, we propose a novel semantically assisted data association strategy that obtains unambiguous persistent object landmarks, and a 2.5D compositional rendering method that enables reliable frame-to-model RGB-D tracking. Consequently, we deliver an optimized online implementation that can run at near frame rate with a single graphics card, and provide a comprehensive evaluation against state of the art baselines. An open source implementation will be provided at https://placeholder." @default.
- W4287605890 created "2022-07-25" @default.
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- W4287605890 date "2020-11-04" @default.
- W4287605890 modified "2023-09-26" @default.
- W4287605890 title "Compositional Scalable Object SLAM" @default.
- W4287605890 doi "https://doi.org/10.48550/arxiv.2011.02658" @default.
- W4287605890 hasPublicationYear "2020" @default.
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