LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving

You are here: Homepage > Search

LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving

Younan, Aarron / El-Kadri, Abdul / Sato, Brad / Nimri, Hisham / Hamieh, Ismail / Tepe, Kemal / Myers, Ryan / Nissan, Selwan / Rahman, Taufiq

Abstract

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines. Performance of the system is scrutinized with regards to hardware capability, software reliability, and real-time performance. The final product is a mobile-platform capable of identifying pedestrians, cars, trucks and cyclists.

Access

Check access

Check availability in my library

Order at Subito €

Export, share and cite

Document information

Title :

LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving

Additional title:

Sae Technical Papers

Contributors:

Younan, Aarron (author) / El-Kadri, Abdul (author) / Sato, Brad (author) / Nimri, Hisham (author) / Hamieh, Ismail (author) / Tepe, Kemal (author) / Myers, Ryan (author) / Nissan, Selwan (author) / Rahman, Taufiq (author)

Conference:

WCX SAE World Congress Experience ; 2020

Published in:

Sae Technical Paper Series

Publication date :

2020-04-14

ISSN :

2688-3627 , 0148-7191

DOI :

https://doi.org/10.4271/2020-01-0136

Type of media :

Conference paper