Map GMU


Students: Yu-Ta Tsai, Pin-Erh Chen, Christopher Vo, Lado Tonia, Sam McKay

Investigators: Fernando Camelli, Jana Kosecka, David Wong and Jyh-Ming Lien


Overview


This page is created to share data captured from the Velodyne HDL-64E LiDAR, LiDAR Velodyne HDL-32e LiDAR, Velodyne VLP-16 Puck, Point Grey Ladybug omnidirectional camera, Go Pro camera, Nikon DSLR camera, Microsoft Kinect sensors, Google Tango tablet development kit.

Our current design of the LiDAR golf cart has the LIDAR scanner and the omnidirectional camera mounted on the front (or back) of the golf cart. The angle of the LIDAR scanner mount can be adjusted between 0 and 90 degrees. This golf cart was evolved from our previous version of the LiDAR pushing cart. A picture of the LiDAR golf cart is shown below.

GMU scanner golf cartGMU scanner golf cart

Videos


This video shows the raw data (Masonvale) captured by our system.



Data


All data collected by our system is shared here.
You will need to use this calibration file with the following pcap files generated by out Velodyne LiDAR.

- Dataset collected in Spring 2016 (pcap and kml files are provided)
- Dataset collected in Summer 2015 (pcap and kml files are provided)
- Smaller and earlier scans

Location pcap data Size Google Map Note
Masonvale pcap.7z 440MB video
Rappahanock pcap.7z 1131MB
Johnson Center pcap.7z 410MB
Patriot Circle pcap.7z 1752MB scanning path
Presidents Park pcap.7z 805MB
Nottoway Annex pcap.7z 259MB scanning path slow, no closed loop
Nottoway Annex pcap.7z 213MB scanning path slow, loop
Nottoway Annex pcap.7z 87MB scanning path fast, loop
Nottoway Annex pcap.7z 186MB scanning path fast, two loops

Images from registered dataset


The following images show the registered point clouds capture from our LiDAR golf cart system in Masonvale at George Mason University.
registered point clouds
Registered Nottoway Annex data: side view
registered point clouds
Registered Nottoway Annex data: side view
registered point clouds
Registered Nottoway Annex data: top view
registered point clouds

Acknowledgement


This project is supported by NSF CNS 1205260
Computer Science @ George Mason University