SplatSearch：使用3D高斯点云和扩散模型的移动机器人实例图像目标导航
SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models
AI总结：
研究移动机器人的实例图像目标导航问题，提出SplatSearch架构，利用3D高斯点云重建和多视角扩散模型，结合前沿探索策略，在多种环境实验中验证其性能优于现有方法。
摘要：
实例图像目标导航（IIN）问题要求部署在未知环境中的移动机器人仅使用目标的单个参考目标图像来搜索特定感兴趣的物体或人员。当参考图像从任意视角拍摄，且机器人必须在稀疏视角场景重建下运行时，这个问题尤其具有挑战性。本文通过引入SplatSearch来解决IIN问题，这是一种利用稀疏视角3D高斯点云（3DGS）重建的新颖架构。SplatSearch使用稀疏在线3DGS地图在候选物体周围渲染多个视角，并使用多视角扩散模型完成渲染图像的缺失区域，从而实现与目标图像的鲁棒特征匹配。还引入了一种新颖的前沿探索策略，利用合成视角的视觉上下文和目标图像的语义上下文来评估前沿位置，使机器人能够优先考虑与目标图像在语义和视觉上相关的前沿。在逼真的家庭和现实世界环境中进行的大量实验验证了SplatSearch在成功率和成功路径长度方面相对于当前最先进方法的更高性能。消融研究证实了SplatSearch的设计选择。
英文摘要：
The Instance Image Goal Navigation (IIN) problem requires mobile robots deployed in unknown environments to search for specific objects or people of interest using only a single reference goal image of the target. This problem can be especially challenging when: 1) the reference image is captured from an arbitrary viewpoint, and 2) the robot must operate with sparse-view scene reconstructions. In this paper, we address the IIN problem, by introducing SplatSearch, a novel architecture that leverages sparse-view 3D Gaussian Splatting (3DGS) reconstructions. SplatSearch renders multiple viewpoints around candidate objects using a sparse online 3DGS map and uses a multi-view diffusion model to complete missing regions of the rendered images, enabling robust feature matching against the goal image. A novel frontier exploration policy is introduced which uses visual context from the synthesized viewpoints with semantic context from the goal image to evaluate frontier locations, allowing the robot to prioritize frontiers that are semantically and visually relevant to the goal image. Extensive experiments in photorealistic home and real-world environments validate the higher performance of SplatSearch against current state-of-the-art methods in terms of Success Rate and Success Path Length. An ablation study confirms the design choices of SplatSearch.
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