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.
The principal method of collecting point cloud 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 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 so robots and autonomous driving computers can understand their environment and navigate through it. The data created during a point cloud survey is good for recognising 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 point cloud 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 both seeing windows and also seeing through them, It also has a myriad of other uses such as seeing the top of treelines and also the ground when flying over with a UAV. A LiDAR point cloud survey can also be conducted to see snow depth. If the LiDAR can see the top layer of snow it can also gets another strong return from the ground beneath allowing the user to calculate depth.
Point cloud survey
Organisations want to collect point cloud data for a range of different reasons. Since LiDAR became a more commercially viable option, it has become a useful tool for surveyors looking for new and innovative ways to perform tasks such as infrastructure and vegetation monitoring or building maps for autonomous driving.
However, to conduct a point cloud survey using a LiDAR, other sensors are required. A LiDAR on its own will give a user a digital picture of its immediate surroundings, but as the LiDAR moves out of range, the picture it is relaying back will no longer exist.
To build up a more permanent picture, that can be saved and reviewed at a later stage, the beams of light emitted from the LiDAR need to be georeferenced with position and inertial data from a GNSS/INS.
OxTS GNSS/INS devices create accurate position and inertial measurement data for this purpose. There are several different models available depending on the use case. The most accurate OxTS GNSS/INS, the RT3000 v4 is used in applications where accuracy is a must, whereas the xNAV650 is suitable for SwaP constrained applications. Then there is also the xRED3000 board set for integrator projects. All devices are compatible with OxTS georeferencing and boresight calibration tool, OxTS Georeferencer. OxTS customers use OxTS Georeferencer to combine GNSS/INS data with LiDAR data to create a georeferenced 3D pointcloud.
Who uses point clouds?
Point cloud data has become an important part of many industry workflows. It gives users accurate, actionable data that they can use repeatedly. The possible use cases for point cloud data are almost endless, however point cloud data is more prevalent in some industries than others. For example:
Construction companies
Construction companies use point cloud data throughout the lifecycle of a project. During the early part of a project, LiDAR data can be utilised for site survey, project planning and to help predict project costs. Furthermore, the construction industry is heavily regulated, it is therefore important that all building work matches the planned designs as closely as possible. Point cloud data can have a crucial role to play in ensuring this.
Civil Engineers
LiDAR has been used in civil engineering now for some time. In civil engineering projects, accuracy is key, particularly at the beginning of a project. In a new project, a LiDAR point cloud can be used to map terrain to help analyse what features, man-made or natural, need to be removed. If the scan is accurate, there will be no need to visit the site again and the data can be repeatedly analysed remotely.
Point cloud data can also be used to ensure that projects remain on schedule by analysing possible repair and maintenance issues earlier in the project.
Architects
In the field of architecture, technology continues to push the boundaries of what is possible and LiDAR plays an important role in this transformation. Point cloud data collected by a LiDAR scanner will give an architect detailed spatial information about their project. Traditionally, architectural surveys have been conducted manually, however with the use of LiDAR, architects can collect vast amounts of data in a fraction of the time.
Point cloud data is also extremely precise, and can enable architects to produce more accurate design drawings. It also helps to reduce the amount of errors therefore improving profitability and reducing the time taken to complete a project.
What formats do point clouds come in?
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.
As mentioned previously, OxTS Georeferencer is a software tool developed in-house by OxTS that dynamically combines GNSS/INS and LiDAR data to create a georeferenced 3D point cloud. The data can be exported in several formats including .LAS, .LAZ and PCD. The data can then be viewed in many third-party point cloud viewer software applications.
.LAS, .LAZ and PCD arent the only point cloud formats in existence. Point cloud data can be exported in many other formats including ASCII (XYZ, OBJ, PTX (Leica), ASC) and Binary (FLS (Faro), PCD as well as LAS.
Precisely Positioned Point Clouds
When architecting your own mobile mapping vehicle with a LiDAR sensor, possibly the most important aspect to get right is your source of localisation. It must enable you to accurately georeference your sensor data to a physical place on Earth. The more accurate the localisation data, the more accurate the point cloud.
This solution brief steps through the aspects that we recommend our customers consider when deciding on the source of localisation for their mobile mapping vehicles.
Read the solution brief to learn how why an OxTS GNSS/INS may be the right option for you.
