F-LOAM: Fast LiDAR Odometry And Mapping

TL;DR

F-LOAM introduces a fast, non-iterative LiDAR odometry and mapping framework that balances high localization accuracy with computational efficiency, suitable for real-time robotic applications.

Contribution

It proposes a novel non-iterative two-stage distortion compensation and feature matching approach for LiDAR SLAM, improving speed without sacrificing accuracy.

Findings

Achieves over 10 Hz processing rate on public datasets.

Maintains competitive localization accuracy in challenging scenarios.

Reduces computational cost compared to traditional iterative methods.

Abstract

Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution that aims to provide a computationally efficient and accurate framework for LiDAR based SLAM. Specifically, we adopt a non-iterative two-stage distortion compensation method to reduce the computational cost. For each scan input, the edge and planar features are extracted and matched to a local edge map and a local plane map separately, where the local smoothness is also considered for iterative pose optimization. Thorough experiments are performed to evaluate its performance in challenging scenarios, including localization for a warehouse Automated Guided Vehicle (AGV) and a public dataset on autonomous driving. The proposed method achieves a competitive localization accuracy with a processing rate of more than 10 Hz in the public dataset evaluation, which provides a good trade-off between performance and computational cost for practical applications.