Pushing the Limits of LiDAR: Accurate Performance Analysis of Indoor 3D LiDARs

Xiting Zhao, Sören Schwertfeger

Keywords: LiDAR Performance Evaluation, Indoor 3D mapping, Point cloud processing, Robotics

TL;DR: This paper presents a comprehensive analysis of indoor LiDAR accuracy and precision across multiple sensors, utilizing a novel experimental methodology and providing a large-scale dataset to advance SLAM and 3D reconstruction algorithms.

Abstract:

Light Detection and Ranging (LiDAR) technology has become crucial in robotics and autonomous systems for generating precise 3D environmental representations. However, challenges persist in achieving high accuracy and precision, especially in indoor environments. This paper rigorously analyzes the performance of various indoor LiDAR systems under different conditions. We present a novel experimental methodology, which quantifies LiDAR accuracy and precision, examining factors such as sensor type, environmental conditions, and target characteristics. Using an extensive dataset collected from nine distinct locations with more than 36000 LiDAR scans, combined with high-precision reference data from a FARO laser scanner, our analysis reveals significant insights into the accuracy and precision across different LiDAR models. The resulting public dataset, which include detailed point clouds and groundtruth labels, are expected to serve as a valuable resource for developing and validating advanced LiDAR processing techniques and benchmarks for various applications. The dataset will be publicly available at http://lidaraccuracy.github.io.

Submission Number: 354