# Evaluation of Lidar-based 3D SLAM algorithms in SubT environment

## TL;DR

This paper presents a comparative experimental study of various 3D Lidar SLAM algorithms in subterranean environments, assessing their pose estimation accuracy and map quality using real-world underground tunnel data.

## Contribution

It provides a systematic evaluation of state-of-the-art Lidar SLAM algorithms in challenging SubT environments, highlighting their strengths and weaknesses for autonomous robot deployment.

## Key findings

- Lidar-inertial algorithms generally outperform lidar-only methods in pose accuracy.
- Certain algorithms produce more accurate and consistent 3D tunnel reconstructions.
- The study identifies the most reliable SLAM algorithms for subterranean navigation.

## Abstract

Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality.