Abstract
This paper presents a novel algorithm for extracting the pose and dimensions of cargo boxes in a large measurement space of a robotic gantry, with sub-centimetre accuracy using multiple low cost RGB-D Kinect sensors. This information is used by a bin-packing and path-planning software to build up a pallet. The robotic gantry workspaces can be up to 10 m in all dimensions, and the cameras cannot be placed top-down since the components of the gantry actuate within this space. This presents a challenge as occlusion and sensor noise is more likely.
This paper presents the system integration components on how point cloud information is extracted from multiple cameras and fused in real-time, how primitives and contours are extracted and corrected using RGB image features, and how cargo parameters from the cluttered cloud are extracted and optimized using graph based segmentation and particle filter based techniques. This is done with sub-centimetre accuracy irrespective of occlusion or noise from cameras at such camera placements and range to cargo.
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This work is funded by the Civil Aviation Authority of Singapore (CAAS) under the Aviation Challenge 2 grant.
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Raaj, Y., Nair, S., Knoll, A. (2017). Precise Measurement of Cargo Boxes for Gantry Robot Palletization in Large Scale Workspaces Using Low-Cost RGB-D Sensors. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10114. Springer, Cham. https://doi.org/10.1007/978-3-319-54190-7_29
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