Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF
arxiv(2024)
摘要
This work proposes a novel approach to bolster both the robot's risk
assessment and safety measures while deepening its understanding of 3D scenes,
which is achieved by leveraging Radiance Field (RF) models and 3D Gaussian
Splatting. To further enhance these capabilities, we incorporate additional
sampled views from the environment with the RF model. One of our key
contributions is the introduction of Risk-aware Environment Masking (RaEM),
which prioritizes crucial information by selecting the next-best-view that
maximizes the expected information gain. This targeted approach aims to
minimize uncertainties surrounding the robot's path and enhance the safety of
its navigation. Our method offers a dual benefit: improved robot safety and
increased efficiency in risk-aware 3D scene reconstruction and understanding.
Extensive experiments in real-world scenarios demonstrate the effectiveness of
our proposed approach, highlighting its potential to establish a robust and
safety-focused framework for active robot exploration and 3D scene
understanding.
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