Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for LiDAR(-Inertial) SLAM

TL;DR

This paper presents a hardware and software system for quantitatively evaluating the accuracy of 3D pointcloud maps from LiDAR(-Inertial) SLAM, using a new outdoor LiDAR target and automated analysis algorithms.

Contribution

It introduces a user-independent evaluation system with a novel outdoor LiDAR target and automated accuracy calculation methods leveraging GPS data.

Findings

The system provides consistent accuracy assessments across users.

It effectively overcomes manual selection limitations.

Two error metrics offer comprehensive accuracy analysis.

Abstract

Accuracy evaluation of a 3D pointcloud map is crucial for the development of autonomous driving systems. In this work, we propose a user-independent software/hardware system that can quantitatively evaluate the accuracy of a 3D pointcloud map acquired from LiDAR(-Inertial) SLAM. We introduce a LiDAR target that functions robustly in the outdoor environment, while remaining observable by LiDAR. We also propose a software algorithm that automatically extracts representative points and calculates the accuracy of the 3D pointcloud map by leveraging GPS position data. This methodology overcomes the limitations of the manual selection method, that its result varies between users. Furthermore, two different error metrics, relative and absolute errors, are introduced to analyze the accuracy from different perspectives. Our implementations are available at: https://github.com/SangwooJung98/3D_Map_Evaluation