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Development of pose estimation system based on dual camera techniques for parameter identification of indoor MAV
a Faculty of Mechanical and Aerospace Enginering, Institut Teknologi 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]An accurate pose estimation system isdeterminant to obtain a good parameteridentification result. Vision-based measurementis an alternative method for UAV’s poseestimation. However, with limited field of viewand vision distance, the vision-basedmeasurement is more suitable for a relativelysmall area or object like indoor MAV.This work presents development of a visionbased measurement system using Dual CameraTechnique for 6- DOF pose estimation. Thesystem consists of two outside-in video cameraswith 30 fps of frame rate, four onboard markers,an image filtering and Direct LinearTransformation algorithm. The system issimulated using 3D software. A 2D pair imagesfrom both video cameras are then processed toreconstruct the position and orientation of theobject.A flight test scenario is virtually simulatedto obtain parameters of aerodynamic andmoment coefficient of a micro coaxialhelicopter. The results show that a vision basedmeasurements offers a relatively straightforward data processing to obtain the defined parameters.[/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]Alternative methods,Direct linear transformation,Dual cameras,Image filtering,Indoor MAV,Pose estimation,Position and orientations,Vision-based measurements[/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]Direct linear transformation,Dual camera technique,Indoor MAV,Parameter identification,Pose estimation[/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][/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][/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]