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

TL;DR

This paper introduces a real-time SLAM framework that uses a novel point cloud downsampling method based on coreset extraction, enabling efficient and accurate odometry and mapping without GPU acceleration.

Contribution

The work presents a coreset-based point cloud downsampling algorithm that preserves quadratic error, facilitating real-time multi-scan registration and SLAM on standard CPUs.

Findings

Outperforms state-of-the-art CPU-based SLAM frameworks

Enables real-time processing without GPU acceleration

Maintains accuracy through exact residual subset extraction

Abstract

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.