Depth Resolution of 3D Imaging Techniques for Target Detection in Mobile Work Machines
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Depth Resolution of 3D Imaging Techniques for Target Detection in Mobile Work Machines. / Suominen, Olli J.; Goncalves Ribeiro, Laura; Gotchev, Atanas.
2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2019. s. 295-300 (International Symposium on Image and Signal Processing and Analysis).Tutkimustuotos › › vertaisarvioitu
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RIS (suitable for import to EndNote) - Lataa
TY - GEN
T1 - Depth Resolution of 3D Imaging Techniques for Target Detection in Mobile Work Machines
AU - Suominen, Olli J.
AU - Goncalves Ribeiro, Laura
AU - Gotchev, Atanas
PY - 2019/10/17
Y1 - 2019/10/17
N2 - 3D imaging is important for enabling autonomous operation of smart mobile work machines. Different sensing techniques have different characteristics, which affect the choice of sensors for each application. We study the depth resolution of three different active imaging technologies and stereoscopic depth estimation with multiple different baselines. We test the effect of depth measurement abilities in measuring average depth, angles between planar structures and diameter from logs. Active sensors show their robustness, while stereoscopic depth estimation follows the error behavior expected from the theory. However, in practice we find that the effect of poor depth resolution can be significantly reduced by averaging over multiple measurements from different points and using more sophisticated stereo processing.
AB - 3D imaging is important for enabling autonomous operation of smart mobile work machines. Different sensing techniques have different characteristics, which affect the choice of sensors for each application. We study the depth resolution of three different active imaging technologies and stereoscopic depth estimation with multiple different baselines. We test the effect of depth measurement abilities in measuring average depth, angles between planar structures and diameter from logs. Active sensors show their robustness, while stereoscopic depth estimation follows the error behavior expected from the theory. However, in practice we find that the effect of poor depth resolution can be significantly reduced by averaging over multiple measurements from different points and using more sophisticated stereo processing.
KW - image resolution
KW - image sensors
KW - industrial robots
KW - object detection
KW - robot vision
KW - stereo image processing
KW - 3D imaging techniques
KW - target detection
KW - autonomous operation
KW - smart mobile work machines
KW - sensing techniques
KW - stereoscopic depth estimation
KW - multiple different baselines
KW - depth measurement abilities
KW - average depth
KW - planar structures
KW - active sensors
KW - poor depth resolution
KW - active imaging technologies
KW - Sensors
KW - Cameras
KW - Measurement
KW - Image resolution
KW - Three-dimensional displays
KW - Pallets
KW - 3D imaging
KW - stereo
KW - Time-of-Flight
KW - LIDAR
KW - depth resolution
U2 - 10.1109/ISPA.2019.8868797
DO - 10.1109/ISPA.2019.8868797
M3 - Conference contribution
SN - 978-1-7281-3141-2
T3 - International Symposium on Image and Signal Processing and Analysis
SP - 295
EP - 300
BT - 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)
PB - IEEE
ER -