SiLVR：面向机器人巡检的、基于神经辐射场的可扩展激光雷达-视觉重建
SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection
AI总结：
本文提出SiLVR，通过融合激光雷达约束、SLAM轨迹引导SfM与子地图扩展NeRF，实现面向机器人巡检的大尺度几何准确、照片级真实重建。
摘要：
我们提出了一种基于神经场的大规模重建系统，该系统融合激光雷达与视觉数据，以生成几何准确并捕捉照片级真实纹理的高质量重建结果。该系统改进了最先进的神经辐射场（NeRF）表示，使其还能纳入激光雷达数据，从而对深度和表面法线施加强几何约束。我们利用来自实时激光雷达SLAM系统的轨迹来引导运动恢复结构（SfM）过程，既显著减少计算时间，又提供对激光雷达深度损失至关重要的度量尺度。我们使用子地图方法，使系统能够扩展到沿长轨迹采集的大规模环境。我们利用安装在腿足机器人上的多相机、激光雷达传感器套件、手持扫描600米建筑场景，以及安装在空中机器人上勘测多层模拟灾难现场建筑的数据，展示了该重建系统。网站：https://ori-drs.github.io/projects/silvr/
英文摘要：
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building. Website: https://ori-drs.github.io/projects/silvr/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
