Volumetric Semantically Consistent 3D Panoptic Mapping
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments. The proposed approach is based on a Voxel-TSDF representation used in recent algorithms. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. Further improvements are achieved by graph optimization-based semantic labeling and instance refinement. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics. We also highlight a downfall in the evaluation of recent studies: using the ground truth trajectory as input instead of a SLAM-estimated one substantially affects the accuracy, creating a large gap between the reported results and the actual performance on real-world data.
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Publication status
published
Editor
Book title
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
12924 - 12931
Publisher
IEEE
Event
37th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
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Methods
Software
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Date created
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Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
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Related publications and datasets
Is supplemented by: https://github.com/y9miao/ConsistentPanopticSLAM