Theses and Dissertations

Issuing Body

Mississippi State University


Bethel, Cindy L.

Committee Member

Tang, Bo

Committee Member

Carruth, Daniel W.

Committee Member

Rahimi, Shahram

Date of Degree


Document Type

Dissertation - Open Access


Computer Science

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Department of Computer Science and Engineering


Obstacle detection and avoidance plays a crucial role in the autonomous navigation of unmanned ground vehicles (UGV). Information about the obstacles decreases as the distance between the UGV and obstacles increases. However, this information decreases much more rapidly for negative obstacles than for positive obstacles. UGV navigation becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., potholes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road environments avoiding negative obstacles in all situations is not possible. In such cases, the local path planner may need to choose a candidate path with a negative obstacle that causes the least amount of damage to the vehicle. To deal better with these types of scenarios, this research introduces a novel approach to perform 3D shape estimation of negative obstacles using LiDAR point cloud data. The dimensions (width, diameter, and depth), location (center), and curvature of negative obstacles were calculated based on an estimated shape. The presented approach can estimate the shape of different kinds of negative obstacles such as holes, trenches, in addition to large and complicated negative obstacles. This approach was tested on different terrain types using the Mississippi Autonomous Vehicle Simulation (MAVS).

Included in

Robotics Commons