Precise Measurement of Cargo Boxes for Gantry Robot Palletization in Large Scale Workspaces Using Low-Cost RGB-D Sensors

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 pathplanning 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 realtime, 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.

Kinect v2 infrared images correction

“…Current robotic applications of the Kinect v2 sensor are mainly based on refinement and correction of the obtained point cloud using PCL or on algorithms available for ROS. 2 The current solutions 3,4,5 described are based on improvements of RGB images gained by Kinect v2.…”