Tampering Detection of LiDAR Data for Autonomous Vehicles

Long, Tao; Xie, Antai; Ren, Xiaoqiang; Wang, Xiaofan

doi:10.23919/ccc52363.2021.9550443

2021 40th Chinese Control Conference (CCC) 2021

DOI: 10.23919/ccc52363.2021.9550443

|View full text |Cite

Tampering Detection of LiDAR Data for Autonomous Vehicles

Tao Long

Antai Xie

Xiaoqiang Ren

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

Alsayed

Nabawy

2022

Drones

View full text Add to dashboard Cite

A low-cost, yet accurate approach for stockpile volume estimation within confined storage spaces is presented. The novel approach relies on actuating a single-point light detecting and ranging (1D LiDAR) sensor using a micro servo motor onboard a drone. The collected LiDAR ranges are converted to a point cloud that allows the reconstruction of 3D stockpiles, hence calculating the volume under the reconstructed surface. The proposed approach was assessed via simulations of a wide range of mission operating conditions while mapping two different stockpile shapes within the Webots robotic simulator. The influences from modulating the drone flight trajectory, servo motion waveform, flight speed, and yawing speed on the mapping performance were all investigated. For simple rectangular trajectories, it was found that having longer trajectories that are adjacent to the storage walls provides best reconstruction results with reasonable energy consumption. On the other hand, for short rectangular trajectories within the storage middle space, the yawing speed at corners must be decreased to ensure good reconstruction quality, although this can lead to relatively high energy consumption. Comparing the volumetric error values, the average error from the proposed 1D LiDAR system, when operating at 6°·s−1 maximum yawing speed at the corners, was 0.8 ± 1.1%, as opposed to 1.8 ± 1.7%, and 0.9 ± 1.0% from the 2D and 3D LiDAR options, respectively. Moreover, compared to 2D and 3D LiDARs, the proposed system requires less scanning speed for data acquisition, is much lighter, and allows a substantial reduction in cost.

show abstract

Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

Alsayed

Nabawy

2022

Drones

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tampering Detection of LiDAR Data for Autonomous Vehicles

Cited by 1 publication

References 17 publications

Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

Indoor Stockpile Reconstruction Using Drone-Borne Actuated Single-Point LiDARs

Contact Info

Product

Resources

About