Bridge Crane Monitoring using a 3D LiDAR and Deep Learning.

Abstract

The use of overhead cranes in warehouses and factories has advantages for handling and transporting bulky and/or heavy loads. But it also involves risks such as collisions with other fixed or mobile elements in the working environment. Different types of sensors have been used for monitoring its operation, mainly artificial vision. In this paper, it is employed a three-dimensional (3D) LiDAR to capture the workspace of a bridge crane. The point clouds generated by this laser sensor are delivered to a convolutional neural network to detect the position of the bridge and its carriage, which allows to locate the hook and the suspended load afterwards. Additionally, the laser scans can also be used to warn the operator of possible collisions with fixed elements of the warehouse. The tests carried out show that the proposed system can be successfully used for monitoring overhead cranes.