When LiDAR data is georeferenced to produce a pointcloud, each frame must be paired with precise navigation data. The relative displacement and orientation between the LiDAR device and the navigation device (INS) must be known. Failure to measure these angles precisely means it is difficult to meet optimal performance. This process can be challenging. However, a quick and efficient data-driven method is available to confirm position and orientation of the LiDAR with respect to the INS, to the level of accuracy necessary to get the best data possible. Here we explore how you can solve the INS and LiDAR calibration conundrum….with data.
Importance of LiDAR and INS Calibration
LiDAR will typically boast high accuracy capabilities of several centimetres in range accuracy per frame. To get this sort of accuracy in a georeferenced pointcloud is much more difficult. It requires very precise navigation data. We can get a quick idea of how precise this needs to be by taking a small angle approximation and multiplying the range by the angle error to get a position error in viewed objects. At 50 m we have an error of about 9 cm if we have an orientation error of just 0.1° in a single axis (0.09 = 50 x 0.1° x 2π/360).
So, you can see that, because the range multiplies the error, it is highly sensitive to the orientation error. Therefore the INS and LiDAR calibration must be as accurate as possible.
An INS device might typically output navigation data with a 0.1° error in its heading.
The navigation data is clearly the limiting factor in getting precise data in your pointcloud. But there is a further complication. When combining LiDAR and navigation data, a relation between the devices is necessary. Surveyors calculating the position of a point in the pointcloud would use the following formula:
Position and orientation of navigation device in the world + relation of LiDAR device with respect to the navigation device + Position of point with respect to the LiDAR device.
The relationship between the devices includes a rotation. This introduces an additional orientational uncertainty that needs to be on the same level of accuracy as the heading and pitch/roll accuracy or it will become the new bottleneck of accuracy in the pointcloud.
This applies to all surveying devices that use a separate navigation device and are viewing objects from a distance, not simply LiDAR.
The ability to measure this angle to the same accuracy is critical to the final accuracy of the pointcloud.
The image here is an example hardware diagram showing the INS position in relation to the LiDAR sensor. This image was taken from within OxTS Georeferencer – our LiDAR georeferencing software.
To measure the rotation of the LiDAR with respect to the INS to 0.1° is not easy. Designing and building CAD printed mounting arrangements can be expensive, time-consuming and provides an inflexible solution. The most efficient way to calibrate the rotation of the LiDAR with respect to the INS is to use a data-driven method, using georeferenced data and an algorithm to calculate the rotation that gives the best pointcloud.
OxTS has developed a method by which a short survey of some calibration targets can be carried out separately (or as part of a longer survey) which is used to calibrate the rotation in post-processing to the degree of accuracy afforded by the georeferenced data.
The method is simple; two flat, square reflective targets are set up. The vehicle, fitted with the LiDAR, is then manoeuvred to survey the targets for a period of 3-10 minutes until an adequate picture of the targets can be georeferenced. The targets can be as simple as wooden boards with reflective tape.
OxTS Georeferencer software searches the georeferenced data for the reflective points belonging to the targets and then uses an algorithm to calculate the rotation that makes the targets as flat as possible.
This is all done with minimal user input and minimum setup time, all that is required is a rigid mounting between the devices so that they cannot move relative to each other. If the hardware setup remains unchanged, the calibration does not need to be repeated.
“As an engineer, I do not know a simple tool which could precisely measure angles between INS and LiDAR to better than a tenth of a degree. OxTS has solved this issue elegantly with the use of tools already present for LiDAR surveying. Running a ten minute data collection between two reflective targets (which are cheap and easy to build) is the most difficult part. Given the well documented OxTS guides, the next step is a simple matter of selecting the collected data files in OxTS Georeferencer and pressing the “Run Boresight Calibration” button. It really is as simple as that and after specifying the calculated angles the pointcloud accuracy improved significantly even visible to the human eye”
Andri Karo, Systems Integration Specialist, Skycorp Oü
An uncalibrated setup can easily be the reason for inaccuracy in a georeferenced pointcloud. As well as navigation data with very high orientation accuracy, the rotation between the LiDAR and the INS must be known to points of a degree. Achieving this can be very difficult. The most effective and efficient way to calibrate the hardware setup is to use the georeferenced LiDAR data itself in a data-driven method. By doing a short survey of reflective targets the setup can be calibrated in minutes ready for high precision surveying.
Product Engineer, OxTS
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