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:
this https URL

本研究提出了一种基于神经场的大规模重建系统，它利用激光雷达和视觉数据生成几何精确且具有照片级真实纹理的高质量重建，同时使用激光雷达 SLAM 系统提供深度和表面法线的强几何约束来改进传统的神经辐射场表示方法。