Abstract
Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance fields (NeRF) have shown promising advances in implicit reconstruction for indoor environments, the problem of simultaneous odometry and mapping for large-scale scenarios using incremental LiDAR data remains unexplored. To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction. All these modules utilize our proposed neural signed distance function, which separates LiDAR points into ground and non-ground points to reduce Z-axis drift, optimizes odometry and voxel embeddings concurrently, and in the end generates dense smooth mesh maps of the environment. Moreover, this joint optimization allows our NeRF-LOAM to be pre-trained free and exhibit strong generalization abilities when applied to different environments. Extensive evaluations on three publicly available datasets demonstrate that our approach achieves state-of-the-art odometry and mapping performance, as well as a strong generalization in large-scale environments utilizing LiDAR data. Furthermore, we perform multiple ablation studies to validate the effectiveness of our network design. The implementation of our approach will be made available at this https URL.
Abstract (translated)
利用LiDAR数据同时进行步进测量和地图绘制是移动设备实现大规模环境完全自主的重要任务。然而,大多数现有的LiDAR-based方法将跟踪质量置于重建质量之上。虽然最近开发的神经网络光流场(NeRF)在在室内环境的隐含重构方面表现出有前途的进展,但使用增量LiDAR数据同时进行步进测量和地图绘制的问题仍未得到探索。为了解决这个问题,在本文中,我们提出了一种基于NeRF的LiDAR步进测量和地图方法NeRF-LOAM,它由三个模块组成:神经网络步进测量、神经网络映射和网格重构。所有这些模块都利用我们提出的神经网络 signed 距离函数,该函数将LiDAR点分为地面和非地面点,以减少Z轴漂移,同时优化步进测量和立方体嵌入,并最终生成环境密度平滑网格地图。此外,这种协同优化允许我们的NeRF-LOAM在自由预训练的情况下表现出先进的步进测量和地图性能,并在使用LiDAR数据进行大规模环境中表现出强泛化能力。我们对三个公开数据集进行了广泛的评估,证明我们的方法和方法实现实现了先进的步进测量和地图性能,并在使用LiDAR数据进行大规模环境中表现出强大的泛化能力。此外,我们进行了多项微分研究来验证我们网络设计的有效性。我们的方法和实现将在此httpsURL上提供。
URL
https://arxiv.org/abs/2303.10709
