Classification Project Page
To operate autonomously, unmanned ground vehicles (UGVs)
must be able to identify the load-bearing surface of the terrain (i.e.
the ground) and obstacles. Current classification, modeling and navigation
techniques work well for structured environments such as urban areas,
where there are roads and obstacles that are usually predictable and
well-defined. However, autonomous navigation in forested terrain presents
many new challenges due to the variability and lack of structure in
This project focuses on:
- Using LIDAR sensing to
classify and model the ground-plane and main tree stems (i.e.
- Utilizing trinocular
vision to enhance classification and modeling techniques
- Implementing a
simultaneous localization and mapping (SLAM) algorithm, using the
location of main tree stems as inputs
- Autonomous navigation
of a UGV in forested terrain
Experimental testing for this project has been performed
on a MobileRobots P3-AT robot platform.
This work is funded by the U.S. Army.