A Pointcloud 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 Pointclouds will have around 100 million points after conducting a survey. Photography can also be overlaid on Pointclouds using photogrammetry techniques to essentially build 3D photography.
The principal method of collecting Pointcloud data is by using LiDAR. LiDAR is a technology that works akin to Radar in that light is sent out from the device and bounces back off objects. The difference is that radio uses large wavelength radiowaves 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 that 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, is the intensity of the reflected beam and indicates the reflectivity of the object.
What are Pointclouds used for?
There are a wide range of applications for which Pointclouds 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 Pointcloud is convenient for recognizing and identifying surfaces and objects; for example, other cars, roadsigns and lane markings. 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 Pointcloud creation for use in surveying. Distances and volumes are easy to calculate using Pointcloud 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 also 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 Pointclouds 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 Pointclouds all the time.
Read the next section in the ‘What is a Pointcloud?’ series: How is a Pointcloud made? (What is a georeferenced Pointcloud?)
The OxTS team regularly present webinars to share knowledge and provide customers with support to get the best from their OxTS products. Here are some of the pointcloud-related webinars you may wish to view, to learn more about this subject:
How to get the best from your data: A boresight calibration demo of how to create a Pointcloud
To get the best position and orientation data possible, survey and mapping professionals must pair their LiDAR devices with an INS. However, pairing LiDAR and INS devices can, by design, create inaccuracies due to offsets that are difficult to measure without some form of data-driven calibration. Join OxTS Product Engineer, Jacob Amacker, to discuss how you can get the best output from your data.
Pointcloud accuracy: How to avoid or mitigate error factors
In order to produce accurate pointclouds, survey and mapping professionals must pair their LiDAR devices with an INS for best position and orientation data. However, many factors can affect the quality of a point cloud. Join Sam Souliman, Senior Support Engineer at OxTS, to discuss the impact of each parameter on the final quality of the point cloud, and provide suggestions to improve the outcome, or mitigate the impact, of each parameter.
Advanced setup for LiDAR surveying, using drones
Join Sam Souliman, Senior Support Engineer at OxTS, to learn how best to configure an OxTS Inertial Navigation System (INS) for LiDAR surveying, using drones. This is an advanced session for experienced NAVconfig users.
Why is it important to use an INS on a mobile mapping vehicle?
Mobile mapping provides a means to quickly and efficiently map a specific area, however when collecting data, the accuracy, precision, and frequency of measurements can vary dramatically depending on the environment where the survey is taking place. Join Paris Austin, OxTS Business Manager for this webinar, when he will discuss why it is important to use an inertial navigation system (INS) when conducting a mobile survey.