DIGITAL TWIN
CASE STUDY

In order to gather the necessary data to create the digital twin, we captured a point cloud of the space.

The client researches many different kinds of robots, including AMR (Automated Mobile Robots) for moving goods around in a facility, as well as stationary robot arms for doing pick and place operations.

The facility includes some office space and cubicles, but is mostly made up of floor space for robot arms and AMR’s and some shelving to allow AMR’s to fetch and place products on shelves. We wanted to capture the entire facility.

The main purpose of the digital twin is primarily for the client to research computer vision and machine learning methods for training robots, so the goal was to produce a high fidelity version of the digital twin using the Unity HDRP (High Definition Rendering Pipeline).

Segmentation means figuring out which parts of the data correspond to which ‘feature’.

It’s common practice to differentiate between which points in the point cloud are the floor, which are the ceiling, walls, windows and door etc.Not only do we want to identify them, but in some cases we want to remove them.

It’s unlikely we’ll be able to scan most places without any people in them and we’re going to need to remove people who happened to walk across the room when we were scanning.

There are also a lot of things which we might want to remove so we can replace them later with better models from elsewhere, and also take advantage of repetition.

An experienced tech artist was given the point clouds, images and reference material and asked to use them to produce the 3D model.

They used a tool called CloudCompare to combine the five different point cloud scans into a single scan that they exported into a format compatible with the 3D modeling tool Blender.

In Blender, they used the point cloud scan as a reference guide to create a 3D model of the facility using traditional CAD tools.