This work presents an algorithm for automated real-time ramp detection using 3D point cloud data in the context of shared-control powered wheelchairs. Basic maneuvering tasks with powered wheelchairs is difficult due to human impairment and control interface characteristics. Although a significant amount of work has been done on obstacle detection and avoidance, much less attention has been given to algorithms for the safe and reliable detection of ramps and inclines; even though navigating these structures is an important part of urban life. We provide an algorithmic solution for accurately detecting traversable inclines for applications with powered wheelchairs using the Point Cloud Library (PCL) within the Robotics Operating System (ROS) framework. All algorithms are implemented first in simulation and later evaluated on data obtained from indoor and outdoor urban environments. We measure the performance of our algorithm with systematic testing on several different ramp structures, observed from varied viewpoints. Results show that our algorithm is successful in detecting the orientation, slope, and width of traversable ramps with up to 100% accuracy and an average detection accuracy of 88%.