Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2409.16944v1
• Date: Wed, 25 Sep 2024 13:56:08 GMT
• Title: Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM
• Authors: Phu Pham, Dipam Patel, Damon Conover, Aniket Bera,
• Abstract summary: Go-SLAM is a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments. Our system facilitates open-vocabulary querying, allowing users to locate objects using natural language descriptions.
• Score: 12.934788858420752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Go-SLAM, a novel framework that utilizes 3D Gaussian Splatting SLAM to reconstruct dynamic environments while embedding object-level information within the scene representations. This framework employs advanced object segmentation techniques, assigning a unique identifier to each Gaussian splat that corresponds to the object it represents. Consequently, our system facilitates open-vocabulary querying, allowing users to locate objects using natural language descriptions. Furthermore, the framework features an optimal path generation module that calculates efficient navigation paths for robots toward queried objects, considering obstacles and environmental uncertainties. Comprehensive evaluations in various scene settings demonstrate the effectiveness of our approach in delivering high-fidelity scene reconstructions, precise object segmentation, flexible object querying, and efficient robot path planning. This work represents an additional step forward in bridging the gap between 3D scene reconstruction, semantic object understanding, and real-time environment interactions.

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