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Using Multi-Quadrotor System for Effective Road Mapping
Renardi B.a, Khosasi E.a, Nazaruddin Y.Y.a, Juliastuti E.a
a Institut Teknologi Bandung, Instrumentation and Control Research Group, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.The development of road infrastructures, especially in developing countries such as Indonesia, continues significantly in the last decade. Along with the acceleration of development that has resulted in 406.14 kilometers of new roads in the last 5 years, an updated road map is needed as rapid as the growth of these roads proportionally. To solve this problem, commonly used technology takes three years to update the map and still unable to map the entire road, especially those that can only be passed by smaller vehicles. In this paper, an alternative mapping technique using multi-quadrotor system is introduced for updating the road map effectively. Multi-quadrotor system allows that the terrain mapping can be larger compared to single quadrotor system. The designed system is an integration of two Robot Operating System (ROS) packages as the framework for the software development. The multi-quadrotor will take images of contour using a 720p front camera with frequency of 2-4 Hz while flying. The images taken with a required specification will be processed into a new map of the area using Agisoft Photoscan. This new map will be processed by a YOLO-based object detection algorithm for specific object identification purpose. Real-Time experimental results using two AR.Drone 2.0 showed that successful image recognition was obtained with high resolution images of map.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]High resolution image,Object detection algorithms,Object identification,Quad rotors,Road infrastructures,Robot operating system,Robot operating systems (ROS),yolo[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ar. drone 2.0,image recognition,multi-quadrotor,robot operating system,yolo[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was supported in part by USAID through the Sustainable Higher Education Research Alliances (SHERA) program under grant number IIE00000078-ITB-1.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8994003[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]