LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913353537945600 |
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| author | Hong, Sheng He, Junjie Zheng, Xinhu Zheng, Chunran Shen, Shaojie |
| author_facet | Hong, Sheng He, Junjie Zheng, Xinhu Zheng, Chunran Shen, Shaojie |
| contents | We introduce an integrated precise LiDAR, Inertial, and Visual (LIV) multimodal sensor fused mapping system that builds on the differentiable \pre{surface splatting }\now{Gaussians} to improve the mapping fidelity, quality, and structural accuracy. Notably, this is also a novel form of tightly coupled map for LiDAR-visual-inertial sensor fusion.
This system leverages the complementary characteristics of LiDAR and visual data to capture the geometric structures of large-scale 3D scenes and restore their visual surface information with high fidelity. The initialization for the scene's surface Gaussians and the sensor's poses of each frame are obtained using a LiDAR-inertial system with the feature of size-adaptive voxels. Then, we optimized and refined the Gaussians using visual-derived photometric gradients to optimize their quality and density.
Our method is compatible with various types of LiDAR, including solid-state and mechanical LiDAR, supporting both repetitive and non-repetitive scanning modes. Bolstering structure construction through LiDAR and facilitating real-time generation of photorealistic renderings across diverse LIV datasets. It showcases notable resilience and versatility in generating real-time photorealistic scenes potentially for digital twins and virtual reality, while also holding potential applicability in real-time SLAM and robotics domains.
We release our software and hardware and self-collected datasets to benefit the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14857 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering Hong, Sheng He, Junjie Zheng, Xinhu Zheng, Chunran Shen, Shaojie Robotics We introduce an integrated precise LiDAR, Inertial, and Visual (LIV) multimodal sensor fused mapping system that builds on the differentiable \pre{surface splatting }\now{Gaussians} to improve the mapping fidelity, quality, and structural accuracy. Notably, this is also a novel form of tightly coupled map for LiDAR-visual-inertial sensor fusion. This system leverages the complementary characteristics of LiDAR and visual data to capture the geometric structures of large-scale 3D scenes and restore their visual surface information with high fidelity. The initialization for the scene's surface Gaussians and the sensor's poses of each frame are obtained using a LiDAR-inertial system with the feature of size-adaptive voxels. Then, we optimized and refined the Gaussians using visual-derived photometric gradients to optimize their quality and density. Our method is compatible with various types of LiDAR, including solid-state and mechanical LiDAR, supporting both repetitive and non-repetitive scanning modes. Bolstering structure construction through LiDAR and facilitating real-time generation of photorealistic renderings across diverse LIV datasets. It showcases notable resilience and versatility in generating real-time photorealistic scenes potentially for digital twins and virtual reality, while also holding potential applicability in real-time SLAM and robotics domains. We release our software and hardware and self-collected datasets to benefit the community. |
| title | LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering |
| topic | Robotics |
| url | https://arxiv.org/abs/2401.14857 |