A LiDAR point cloud is a collection of millions (even billions) of points that is used to accurately map an environment in a way similarly to how a pointillism painting creates a picture. Each of these points have a location in a known coordinate system (local or global), along with a value for intensity which quantifies the amount of light energy that is recorded by the scanner. A LiDAR point cloud is the product of sensor fusion across a GPS-Aided Inertial Navigation System (INS) and a LiDAR scanner. Each sensor plays a critical role in how a LiDAR payload functions and the applicability of its point cloud output.
LiDAR stands for light detection and ranging, meaning that a LiDAR scanner uses light to determine the distance between two objects. Since the speed of light is a quantifiable value, the distance of an object is determined by the time it takes for light to reach the object, reflect off it, and return to the scanner. In the context of a mapping project, the LiDAR scanner emits 905 mm or 1505 mm wavelength lasers and calculates distances of points across the scanned area. That is the extent of the capabilities of a LiDAR scanner alone, a conglomerate of distance measurements without any concept of the space in which the payload is operating (calculates scalar but not vector). Therefore, the LiDAR is clearly just part of the solution, thus illustrating the need for additional sensors to produce a complete product.
A GPS-Aided INS is composed of an inertial measurement unit (IMU) containing three-axis accelerometers and gyroscopes, along with a GNSS receiver. After initial orientation and bias estimation, data from the IMU and GNSS receiver are fed through a robust Kalman filter algorithm, in which the unit will begin outputting valid and accurate orientation, position, velocity and timing. In the scope of a LiDAR payload, the INS is important for accurate georeferencing of data. Georeferencing is the process of applying a coordinate system to the point cloud so it can be accurately related to a geographic (or local) coordinate system. The GNSS receiver is responsible for obtaining a known global position of the rover in which the payload is mounted to. The IMU is correspondingly responsible for then taking that known position and transferring it to accurately georeference the acquired LiDAR data point. Add a datalogger, and this system now logs point clouds consisting of points that each have a location in space. The need for accurate georeferencing highlights the importance for a quality IMU, GNSS receiver, accurate boresighting (alignment of the LiDAR to the IMU), IMU-GNSS antenna offset calculation, and vehicle-payload rotation compensation. These are the critical components, and processes that if not carefully selected, calculated and compensated for, will result in an inaccurate point cloud… Full article
Headline image – Courtesy of Inertial Labs, 2021
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