用高斯点云构建视觉导航先验，实现高效实时机器人寻图
BEINGS: Bayesian Embodied Image-goal Navigation with Gaussian Splatting
- 将图像目标导航建模为模型预测控制问题，结合贝叶斯更新动态调整策略
- 利用3D高斯点云作为场景先验，实时预测未来观测，提升复杂环境导航效率
- 无需大量数据训练，在仿真与真实机器人上均表现稳定，适合视觉复杂场景
图像目标导航使机器人根据视觉线索抵达目标图像拍摄位置。然而，现有方法或高度依赖数据且计算开销大，或在复杂环境中因探索策略不足而效率低下。为此，我们提出基于高斯点云的贝叶斯具身图像目标导航（BEINGS），将ImageNav建模为模型预测控制框架下的最优控制问题。该方法利用3D高斯点云作为场景先验，预测未来观测，使导航决策基于机器人感知经验实现高效实时响应。通过贝叶斯更新，系统可动态优化策略，无需大量先验经验或数据。算法在大量仿真与物理实验中验证，展现出在视觉复杂场景下对具身机器人系统的应用潜力。
原文摘要 · Abstract (English)
Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient exploration strategies. To address these limitations, we propose Bayesian Embodied Image-goal Navigation Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal control problem within a model predictive control framework. BEINGS leverages 3D Gaussian Splatting as a scene prior to predict future observations, enabling efficient, real-time navigation decisions grounded in the robot's sensory experiences. By integrating Bayesian updates, our method dynamically refines the robot's strategy without requiring extensive prior experience or data. Our algorithm is validated through extensive simulations and physical experiments, showcasing its potential for embodied robot systems in visually complex scenarios.
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