CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 10799 > Article
Presentation
11 October 2018 Real-time photon-counting LiDAR enhanced with deep-learning (Conference Presentation)
Matthew P. Edgar, Miles Padgett, Catherine Higham, Roderick Murray-Smith
Author Affiliations +
Proceedings Volume 10799, Emerging Imaging and Sensing Technologies for Security and Defence III; and Unmanned Sensors, Systems, and Countermeasures; 107990B (2018) https://doi.org/10.1117/12.2503325
Event: SPIE Security + Defence, 2018, Berlin, Germany
Abstract
An increasing demand for high-performance and lower-cost LiDAR has led to a wealth of new research and commercial technologies utilizing photon-counting detection and image reconstruction techniques. In this work we demonstrate results from a compact and portable photon-counting LiDAR prototype, consisting of a high-speed digital-micromirror-device, short-pulsed infrared laser, photon-counting photomultiplier and FPGA-based TCSPC electronics. We evaluate the system performance when operating at ranges of up to 20m, using different scanning and reconstruction techniques which employ compressed sensing to increase the frame rate. Deep neural networks are computational models for learning representations of data with multiple levels of abstraction. Recently, there has been interest in using deep neural networks as a promising alternative to traditional compressive sensing techniques. In this work we will demonstrate progress made in using a deep convolutional auto-encoder network for recovering 3D images from a photon-counting LiDAR, which provides a computationally-efficient pipeline for solving underdetermined problems with better quality, in real-time. We anticipate that low-cost photon-counting LiDAR, enriched by deep-learning, will play an important role in many commercial sensing applications such as autonomous vehicles.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Matthew P. Edgar, Miles Padgett, Catherine Higham, and Roderick Murray-Smith "Real-time photon-counting LiDAR enhanced with deep-learning (Conference Presentation)", Proc. SPIE 10799, Emerging Imaging and Sensing Technologies for Security and Defence III; and Unmanned Sensors, Systems, and Countermeasures, 107990B (11 October 2018); https://doi.org/10.1117/12.2503325
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
 PRESENTATION
WATCH
PRESENTATION SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
LIDAR
Compressed sensing
Data modeling
Image restoration
Neural networks
Digital electronics
Infrared lasers
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top