A Searching Space Constrained Partial to Full Registration Approach With Applications in Airport Trolley Deployment Robot

For airports with high passenger and luggage flows, a large number of staff members have to be hired to deploy the scattered passenger luggage trolleys. To release humans from the repetitive and laborious job, we develop an autonomous trolley deployment robot to detect, transport and collect the scattered idle trolleys to recycling points. This paper will firstly illustrate the entire collection pipeline of the deployment robot system and then address the key challenge: partial to full point set registration. With the perception framework, the robot can detect the idle trolleys and acquire the pose of the trolleys on the ground, and then capture the trolley from behind, along the same direction for subsequent grasping and manipulation. With RGB-D camera and a segmentation Convolutional Neural Network, the robot can generate a partial surface point cloud of the detected trolley. The resulting point cloud,
data
and a pre-scanned full trolley point cloud,
model
, are matched by an implicit pose. To tackle the low accuracy and long computation time issues, a novel searching space-constrained point set registration algorithm is proposed to register the two overlapping point sets. Based on Branch-and-Bound (BnB) mechanism, the error between
data
and
model
is iteratively optimized. The constraint of searching space speeds up the global searching of the optimal pose, by pruning the candidate spaces which is impossible to contain the optimal result. To evaluate the performance, an airport trolley segmentation dataset and a point cloud dataset for registration are constructed. Experimental results on the datasets and synthetic dataset show that our method achieves higher accuracy and success rate than the previous methods. The experiments demonstrated in video clips validate the developed system works in real-world applications.