Computer Science > Computer Vision and Pattern Recognition
arXiv:2411.15043 (cs)
[Submitted on 22 Nov 2024 (v1), last revised 10 Mar 2025 (this version, v2)]
Title:Open-Vocabulary Online Semantic Mapping for SLAM
View a PDF of the paper titled Open-Vocabulary Online Semantic Mapping for SLAM, by Tomas Berriel Martins and 2 other authors
View PDFAbstract:This paper presents an Open-Vocabulary Online 3D semantic mapping pipeline, that we denote by its acronym OVO. Given a sequence of posed RGB-D frames, we detect and track 3D segments, which we describe using CLIP vectors. These are computed from the viewpoints where they are observed by a novel CLIP merging method. Notably, our OVO has a significantly lower computational and memory footprint than offline baselines, while also showing better segmentation metrics than them. Along with superior segmentation performance, we also show experimental results of our mapping contributions integrated with two different SLAM backbones (Gaussian-SLAM and ORB-SLAM2), being the first ones demonstrating end-to-end open-vocabulary online 3D reconstructions without relying on ground-truth camera poses or scene geometry.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) |
| Cite as: | arXiv:2411.15043 [cs.CV] |
| (orarXiv:2411.15043v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2411.15043 arXiv-issued DOI via DataCite |
Submission history
From: Tomás Berriel Martins [view email][v1] Fri, 22 Nov 2024 16:25:05 UTC (5,713 KB)
[v2] Mon, 10 Mar 2025 14:44:38 UTC (17,083 KB)
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View a PDF of the paper titled Open-Vocabulary Online Semantic Mapping for SLAM, by Tomas Berriel Martins and 2 other authors
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