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

2025-05-02

AI Summary

Real-time multi-scan point cloud registration in LiDAR-inertial SLAM faces a fundamental trade-off between high computational complexity and approximation errors introduced by conventional subsampling. Method: We propose a fully CPU-based tightly coupled LiDAR-inertial SLAM system that (1) employs a coreset-theoretic exact downsampling algorithm, preserving the original quadratic residual function without any approximation error, and (2) integrates sliding-window odometry, factor-graph-based global optimization, and multi-frame joint registration to enable real-time full-map optimization. Contribution/Results: To our knowledge, this is the first CPU-only SLAM system achieving real-time operation with global consistency. It significantly outperforms existing CPU-based methods in both accuracy and robustness, establishing a new high-fidelity mapping paradigm for resource-constrained platforms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Real-time multi-scan registration error minimization
• Exact point cloud downsampling without approximation errors
• CPU-based SLAM outperforming state-of-the-art frameworks

Innovation

• Coreset-based exact point cloud downsampling algorithm
• Real-time SLAM with sliding window optimization
• CPU-efficient global trajectory optimization