Error analysis and simulation of LiDAR system for indoor detection based on robotic dog

Yufei Liu; Yilin Liu; Pengfei Jiang; Zhiming Xiong

doi:10.1117/12.2684919

19 October 2023 Error analysis and simulation of LiDAR system for indoor detection based on robotic dog

Yufei Liu, Yilin Liu, Pengfei Jiang, Zhiming Xiong

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127094V (2023) https://doi.org/10.1117/12.2684919
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The robotic dog is an advanced mobile robot that employs cutting-edge electromechanical and control technologies, making it highly adept at indoor tasks such as detection, reconnaissance, and mapping. LiDAR sensors have become increasingly popular in unmanned platforms such as drones and unmanned vehicles, thanks to their high resolution, strong anti-interference capabilities, and fast response speeds. However, the machine dog's greater mechanical degrees of freedom and posture angle settlement errors in indoor environments without GNSS pose significant challenges for achieving precise navigation. Moreover, due to the complexity of the machine dog's system and positioning errors, the point cloud positioning accuracy of the LiDAR is susceptible to various errors. This article comprehensively examines errors in the multi-line LiDAR measurement system, installation, unmanned platform positioning, and posture, and quantifies their impact on the point cloud positioning accuracy of the LiDAR, culminating in an indoor machine dog LiDAR detection error model. The simulation reveals that posture solution errors and radar scanning angles are critical sources of point cloud positioning errors. Additionally, the shape of the detection target plays a role in the point cloud distribution on the surface, with a larger angle between the target surface normal and the LiDAR system main axis resulting in a sparser point cloud distribution and lower accuracy of positioning and mapping. The research findings have significant implications for the development and enhancement of indoor machine dog LiDAR systems.

Citation Download Citation

Yufei Liu, Yilin Liu, Pengfei Jiang, and Zhiming Xiong "Error analysis and simulation of LiDAR system for indoor detection based on robotic dog", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127094V (19 October 2023); https://doi.org/10.1117/12.2684919

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

LIDAR

Point clouds

Error analysis

Laser systems engineering

Robotics

Target detection

Systems modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years