Point cloud refers to a collection of 3D points that represent the shape and structure of an environment. These points are typically generated by LiDAR or 3D scanning systems, and each point contains spatial coordinates (X, Y, Z), sometimes along with additional attributes like intensity or color. While the LiDAR sensor captures the raw spatial data, it is the inertial navigation system (INS) that provides the precise position and orientation of the sensor at every moment. This is crucial because, to accurately place each point in a global reference frame, the system needs to know exactly where the scanner was and how it was oriented when each measurement was taken.
The INS, which combines data from accelerometers and gyroscopes (and often GNSS receivers), continuously tracks the motion of the platform, whether it’s an aircraft, vehicle, drone, or vessel. The LiDAR captures millions of points per second, while the INS simultaneously delivers real-time position and attitude information, enabling the system to correctly georeference each point. This process results in a highly accurate and spatially consistent point cloud.
By integrating inertial data with LiDAR, users can generate detailed 3D representations of complex environments, even in GNSS-denied areas or during rapid motion. This is especially important in mobile mapping, precision surveying, autonomous navigation, and environmental modeling. For example, a UAV equipped with an INS and LiDAR can map a forest canopy or powerline corridor with centimeter-level accuracy, even when flying over rugged or remote terrain.
Similarly, a mobile mapping vehicle can scan urban environments in real time, with the INS ensuring that the resulting point cloud remains coherent and aligned despite changes in speed, direction, or terrain.
How do LiDAR or imagining systems generate point clouds?
A point cloud works by capturing a dense set of individual points in 3D space to represent the surface of objects or environments. Each point in the cloud holds spatial coordinates—X, Y, and Z—that define its position.
Sensors such as LiDAR (Light Detection and Ranging) or 3D cameras usually generate these points by scanning the surroundings with laser pulses or using stereo imaging to measure distances to surfaces. As the sensor collects data, it calculates how long it takes for the signal to return, allowing it to determine the exact position of each point in space.
When the sensor moves—on a vehicle, UAV, or handheld device—it continuously collects new data points from different angles. The system records the timestamp of each point and uses the sensor’s position and orientation at the moment of capture to reconstruct an accurate 3D model.
That’s where inertial navigation systems (INS) or GNSS/INS integrations come in. The system tracks the sensor’s movement in real time, allowing it to georeference the point cloud data and align it accurately with the real-world coordinate system.
Once captured and processed, a point cloud provides a rich, detailed digital replica of the scanned environment.Users can apply these data sets to create 3D maps, perform measurements, model buildings and terrain, analyze structural changes, and enable navigation in autonomous systems. The denser the point cloud, the more detailed and accurate the resulting 3D model will be.
In essence, point clouds work by combining laser or imaging measurements with real-time positioning data to create a detailed and spatially accurate 3D view of the world.