F-LOAM : Fast LiDAR Odometry and Mapping

By Han Wang, Chen Wang, Chun-Lin Chen, Lihua Xie. IEEE/RJS International Conference on Intelligent RObots and Systems, 2021

390 citations recorded by the source corpus.

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. It is one of the most accurate and fastest open-sourced SLAM systems1 in KITTI dataset ranking.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/1cda0ccd00cc09d3f8996663bb1d38da5a09fa93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Cyrill Stachniss
2. Jens Behley
3. Ignacio Vizzo
4. Tiziano Guadagnino
5. Louis Wiesmann
6. Benedikt Mersch

In Electrical and Electronic Engineering

1. Cyrill Stachniss
2. Jens Behley

In Environmental Engineering

1. Cyrill Stachniss
2. Jens Behley
3. Ignacio Vizzo
4. Tiziano Guadagnino
5. Louis Wiesmann
6. Benedikt Mersch

Citations in the corpus

• KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way (2022, methodology): “For this experiment, we compare against state-of-the-art odometry systems, namely MULLS [21], SuMa [1], F-LOAM [33], and CT-ICP [10].”