提出精确点云降采样方法，实现无误差实时惯性里程计与建图。
Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling
- 基于核集提取的降采样算法，保持原始误差函数不变。
- 在标准CPU上实现全地图注册误差优化，实时运行且精度领先。
- 适合资源受限场景下的高精度实时定位，无需GPU加速。
为支持因子图上多扫描匹配误差最小化的实时处理，本文设计了一种基于核集提取的点云降采样算法。该算法从输入点中提取残差子集，使子集在给定位姿下产生的二次误差函数与原集合完全一致，从而在不引入近似误差的前提下大幅减少需评估的残差数量。基于此算法，构建了一个完整的SLAM框架，包含基于滑动窗口优化的里程计估计和基于全局地图注册误差最小化的轨迹优化，两者均能在标准CPU上实时运行。实验表明，所提框架在不使用GPU加速的情况下，性能优于现有最先进的基于CPU的SLAM系统。
原文摘要 · Abstract (English)
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.
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