ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM

TL;DR

ART-SLAM is a fast, accurate, and modular LiDAR SLAM system that improves real-time 6DoF pose estimation for ground vehicles, outperforming existing methods in accuracy and robustness without relying on loop closure.

Contribution

The paper introduces a novel LiDAR SLAM pipeline that combines filtering, pre-tracking, and efficient pose graph optimization, achieving real-time performance with high accuracy.

Findings

Achieves equal or better accuracy than state-of-the-art methods.

Handles cases without loop closure effectively.

Operates efficiently in real-time on standard datasets.

Abstract

Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems exist already, they are either not accurate or they struggle in real-time setting. In this paper, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking and floor detection, to ground-optimize the estimated trajectory. The availability of a pre-tracker, working in parallel with the filtering process, allows to obtain pre-computed odometries, to be used as aids when performing tracking. Efficient loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI and RADIATE datasets.