Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling

-- In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset extraction. This algorithm extracts a subset of the residuals of input points such that the subset yields exactly the same quadratic error function as that of the original set for a given pose. This enables a significant reduction in the number of residuals to be evaluated without approximation errors at the sampling point. Using this algorithm, we devise a complete SLAM framework that consists of odometry estimation based on sliding window optimization and global trajectory optimization based on registration error minimization over the entire map, both of which can run in real time on a standard CPU. The experimental results demonstrate that the proposed framework outperforms state-of-the-art CPU-based SLAM frameworks without the use of GPU acceleration. Point cloud SLAM algorithms that directly compute and minimize point cloud registration errors on factor graphs have been gaining attention due to their precision and robustness. Methods such as odometry estimation with sliding window optimization [1], global trajectory optimization via global registration error minimization [2], and LiDAR-bundle adjustment [3] excel in optimizing sensor poses to maximize the consistency between multiple point clouds. They offer more accurate and reliable estimations compared to those obtained using traditional approaches such as filtering-based odometry estimation [4] and global trajectory optimization using relative pose constraints [5].