Automated terrain mapping based on mask R-CNN neural network

Abstract

PurposeThe purpose of this paper is to analyze the development of a novel approach for automated terrain mapping a robotic vehicles path tracing.Design/methodology/approachThe approach includes stitching of images, obtained from unmanned aerial vehicle, based on ORB descriptors, into an orthomosaic image and the GPS-coordinates are binded to the corresponding pixels of the map. The obtained image is fed to a neural network MASK R-CNN for detection and classification regions, which are potentially dangerous for robotic vehicles motion. To visualize the obtained map and obstacles on it, the authors propose their own application architecture. Users can any time edit the present areas or add new ones, which are not intended for robotic vehicles traffic. Then the GPS-coordinates of these areas are passed to robotic vehicles and the optimal route is traced based on this dataFindingsThe developed approach allows revealing impassable regions on terrain map and associating them with GPS-coordinates, whereas these regions can be edited by the user.Practical implicationsThe total duration of the algorithm, including the step with Mask R-CNN network on the same dataset of 120 items was 7.5 s.Originality/valueCreating an orthophotomap from 120 images with image resolution of 470 × 425 px requires less than 6 s on a laptop with moderate computing power, what justifies using such algorithms in the field without any powerful and expensive hardware.