This paper presents a prototype system using a commercially available drone for semi-autonomous point cloud mapping, for which post-processing of data is of key importance to achieve sufficient accuracy. The focus of this work is to improve the result by updating the software for drone control and post-processing. The mapping is done by utilizing a mobile app that initiates a drone movement such as to move one meter forward or 90 ∘ rotation. There is also an option to utilize the ultrasonic sensors and rotate 360 ∘, in increments, to map the surroundings area. 3D vertices retrieved from the mapping is stored into a database. A Python app is later utilized to retrieve mapped vertices where post-processing and visualization methods are applied. The result can be stored as a file and can be imported into other software for further processing. The primary value of the proposed solution is that it is a very cost-effective method for mapping spaces in 3D and then generating a mesh for further processing.