A fast, complete, point cloud based loop closure for LiDAR odometry and mapping

TL;DR

This paper introduces a fast, rotation-invariant loop closure method for LiDAR odometry that uses 2D histogram cross-correlation, improving accuracy and reliability in long-term mapping.

Contribution

It presents a novel 2D histogram-based loop closure technique integrated into LOAM, offering a complete, practical, and open-source solution for LiDAR-based SLAM.

Findings

The method is fast and rotation-invariant.

It reliably detects loops in LiDAR odometry.

The system is open source and ready for deployment.

Abstract

This paper presents a loop closure method to correct the long-term drift in LiDAR odometry and mapping (LOAM). Our proposed method computes the 2D histogram of keyframes, a local map patch, and uses the normalized cross-correlation of the 2D histograms as the similarity metric between the current keyframe and those in the map. We show that this method is fast, invariant to rotation, and produces reliable and accurate loop detection. The proposed method is implemented with careful engineering and integrated into the LOAM algorithm, forming a complete and practical system ready to use. To benefit the community by serving a benchmark for loop closure, the entire system is made open source on Github