Point clouds are a common data type in robotics applications. They allow a robot to “see” its environment. Unfortunately, its use for path planning is somewhat limited. There is just too much data for a robot to look through to calculate a path in a timely fashion. The objective of this research project is to create an algorithm that can take a 3D point cloud data set and convert it into a 2D occupancy grid, a much more common data type for navigation/path planning algorithms. The algorithm is named Cloud To Map. The development for this project proceeds according to the software development lifecycle. After extensive research, a list of requirements is developed. The algorithm is then designed and implemented. Subsequently, testing is done to ensure that the implementation satisfies the project requirements. During the testing phase, if any requirements are left unsatisfied, this process is then repeated. The research is ongoing. The first iteration of the algorithm is only capable of converting point clouds output by a specific application. Work is being done to allow it to convert point clouds from any source. While conversion algorithms like this one have been developed before, Cloud To Map has a broader range of applications. Upon completion, the project package will be published to ROS.org, which will make it available to developers around the world as a solution to the issue defined above.