Rapid-Mapping: LiDAR-Visual Implicit Neural Representations for Real-Time Dense Mapping

Abstract

Real-time dense mapping with high-fidelity textures in large-scale environments is such a challenge in robots, digital twins, and AR/VR applications. Neural Radiance Field (NeRF) has demonstrated remarkable capabilities in capturing intricate details and saving memory space, which provides significant advantages in the fine-grained reconstruction of large-scale scenes. Existing LiDAR-based mapping methods have not harnessed NeRF's ability to capture textures. In this letter, we propose the first real-time LiDAR-Visual mapping method in large-scale indoor and outdoor environments, named Rapid-Mapping, that utilizes implicit neural representations and preserves high-fidelity textures. First of all, to align the camera image and LiDAR depth, we propose a method for extrinsic refinement to mitigate the issue of texture blurring caused by long-range and extended temporal scale measurements. Also, to alleviate the effects of lighting conditions and camera hardware interference, we utilize prior hue information to constrain the inverse affine transformation. Extensive experiments validate that Rapid-Mapping enables real-time dense mapping in large-scale complex indoor and outdoor scenes, exhibiting more detailed realistic textures and more accurate geometry compared to existing methods.