Accurate and affordable indoor 3D reconstruction is critical for effective robot navigation and interaction. Traditional LiDAR-based mapping provides high precision but is costly, heavy, and power-intensive, with limited ability for novel view rendering. Vision-based mapping, while cost-effective and capable of capturing visual data, often struggles with high-quality 3D reconstruction due to sparse point clouds. We propose ES-Gaussian, an end-to-end system using a low-altitude camera and single-line LiDAR for high-quality 3D indoor reconstruction. Our system features Visual Error Construction (VEC) to enhance sparse point clouds by identifying and correcting areas with insufficient geometric detail from 2D error maps. Additionally, we introduce a novel 3DGS initialization method guided by single-line LiDAR, overcoming the limitations of traditional multi-view setups and enabling effective reconstruction in resource-constrained environments. Extensive experimental results on our new Dreame-SR dataset and a publicly available dataset demonstrate that ES-Gaussian outperforms existing methods, particularly in challenging scenarios.
精确且经济的室内3D重建对于机器人导航和交互至关重要。传统的基于LiDAR的地图构建虽然具有高精度，但成本高、重量大且功耗高，并且在新视角渲染方面能力有限。基于视觉的地图构建成本较低，能够捕捉视觉数据，但由于点云稀疏，往往难以实现高质量的3D重建。我们提出了ES-Gaussian，这是一个端到端系统，使用低空摄像头和单线LiDAR实现高质量的室内3D重建。该系统的特点是引入视觉误差构建（VEC）模块，通过2D误差图识别并修正几何细节不足的区域，从而增强稀疏点云。此外，我们提出了一种基于单线LiDAR引导的3DGS初始化方法，克服了传统多视角设置的局限性，使其能够在资源受限的环境中进行有效重建。我们在新的Dreame-SR数据集和一个公开数据集上进行了广泛的实验，结果表明，ES-Gaussian在特别具有挑战性的场景中优于现有方法。