The value of point clouds is unlimited

August 25, 2020  - By

By Jacob Amacker
Oxford Technical Solutions

What is a point cloud?

A point cloud is fundamentally a simple construct. It is a collection of points in 3D space, each point being given a coordinate in Cartesian convention. The points can also be given other properties, often these will be indicative of how they were obtained.

Examples might include the time at which they were “seen” by the surveying device that collected the data. The intensity or error in position that the point has might also be included.

Often point clouds will have around 100 million points after conducting a survey. Photography can also be overlaid on point clouds using photogrammetry techniques to essentially build 3D photography.

Image: OxTS

Image: OxTS

INS survey: point clouds

The principal method of collecting point-cloud data is by using lidar. Lidar technology is akin to radar: light is sent out from the device and bounces back off of objects. The difference is that radio uses large wavelength radio waves and lidar uses small wavelength lasers for high precision.

The time for light to return to the device is used with the speed of light to calculate the distance away. Typically, a lidar device will contain lasers with a fixed vertical angle, but which spin around in the horizontal plane. Internally, the device knows at what angle the laser is pointing vertically and its azimuth angle. This gives the device the position of the point on the object in 3D spherical coordinates.

The lasers inside produce thousands of points per second. Intensity, mentioned above, refers to the intensity of the reflected beam and indicates the reflectivity of the object.

What is a georeferenced point cloud?

Lidar requires navigation data to conduct a survey. We combine the navigation data with the lidar data to create georeferenced point clouds. Lidar devices know where points are in relation to each other, but they need to be told where they are in the world to be able to build a point cloud while moving the lidar.

The navigation data often comes from an inertial navigation system (INS). An INS is a sophisticated combiner of inertial measurement unit (IMU) and GNSS data to get the best navigation data — so a device knows where it is in the world and how it is moving.

The coordinates from the INS are added vectorially to the point coordinates of the lidar to get the final coordinates that would be used in the point cloud. This allows a user to put their lidar device on a vehicle like a van or an unmanned aerial vehicle (UAV) with an INS, to survey large areas efficiently instead of doing multiple static surveys and stitching them together.

Photo: OxTS

Photo: OxTS

What are point clouds used for?

There are a wide range of applications for which point clouds can be used. They are increasingly used in real time for robots and autonomous driving computers to understand their environment and navigate through it. The data in a point clouds is convenient for recognizing and identifying surfaces and objects; for example, other cars, road signs and lane markings.

OxTS has been a global leader in inertial and GNSS technologies since 1998. OxTS is fundamentally involved in helping car manufacturers get the navigation data they require to go with lidar data in autonomous vehicle development, and in point clouds creation for use in surveying.

Distances and volumes are easy to calculate using point-cloud analysis software, and intensity can help identify different materials.

Another feature that lidar offers is multi-returns. This allows a laser pulse (which has a finite cross-section) to bounce back off of multiple surfaces to give multiple points from the same pulse. This is particularly useful for seeing windows and also seeing through them, and also for a myriad of other uses such as seeing the top of a treeline and the ground when flying over with a UAV.

It can also be used to see snow depth. The lidar can see the top layer of snow and also gets another strong return from the ground beneath.

At OxTS, we see lidar point clouds being used for driverless-car and work-vehicle development, coastal and forest management, infrastructure monitoring (signs, drains, bridges, road surfaces, railroads, etc.), creating 3D models of cities, pipeline exploration and more.

The final product is a simple file format, for which the possibilities are almost endless — and we see new applications using point clouds all the time.