RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911191365844992 |
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| author | Tao, Yuezhan Ong, Dexter Murali, Varun Spasojevic, Igor Chaudhari, Pratik Kumar, Vijay |
| author_facet | Tao, Yuezhan Ong, Dexter Murali, Varun Spasojevic, Igor Chaudhari, Pratik Kumar, Vijay |
| contents | We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing dense maps. Further, we develop a GPU-accelerated motion planning algorithm that can exploit the Gaussian map for real-time navigation. The Gaussian map constructed onboard the robot is optimized for both photometric and geometric quality while enabling real-time situational awareness for autonomy. We show through viewpoint selection experiments that our method yields comparable Peak Signal-to-Noise Ratio (PSNR) and similar reconstruction error to state-of-the-art approaches, while being orders of magnitude faster to compute. In closed-loop physics-based simulation and real-world experiments, our algorithm achieves better map quality (at least 0.8dB higher PSNR and more than 16% higher geometric reconstruction accuracy) than maps constructed by a state-of-the-art method, enabling semantic segmentation using off-the-shelf open-set models. Experiment videos and more details can be found on our project page: https://tyuezhan.github.io/RT GuIDE/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration Tao, Yuezhan Ong, Dexter Murali, Varun Spasojevic, Igor Chaudhari, Pratik Kumar, Vijay Robotics We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing dense maps. Further, we develop a GPU-accelerated motion planning algorithm that can exploit the Gaussian map for real-time navigation. The Gaussian map constructed onboard the robot is optimized for both photometric and geometric quality while enabling real-time situational awareness for autonomy. We show through viewpoint selection experiments that our method yields comparable Peak Signal-to-Noise Ratio (PSNR) and similar reconstruction error to state-of-the-art approaches, while being orders of magnitude faster to compute. In closed-loop physics-based simulation and real-world experiments, our algorithm achieves better map quality (at least 0.8dB higher PSNR and more than 16% higher geometric reconstruction accuracy) than maps constructed by a state-of-the-art method, enabling semantic segmentation using off-the-shelf open-set models. Experiment videos and more details can be found on our project page: https://tyuezhan.github.io/RT GuIDE/ |
| title | RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.18122 |