LiDAR UAV Integration for Georeferenced Point Clouds

December 19, 2022
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Updated February 9, 2026
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2 min read

diagram supporting How to Integrate a Lidar Sensor with a UAV
The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user’s needs.

From a paper by Del Savio et al.

A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system’s accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented.

Future research studies may consider using high-precision GNSS/INS sensors with an error of fewer than 0.1 degrees in orientation measurements and 5 cm in position measurements to improve the accuracy of the point cloud. Path generation can also be enhanced by processing sensor position and orientation data through a particle filter to better model sensor uncertainty. This would be supported by developing a particle filter framework for solving positioning, navigation and tracking issues (the Monte Carlo sequential methods), which numerically illustrates its advantages over classical algorithms based on Kalman filters in airborne applications.

For the complete paper on how to integrate a lidar sensor with a UAS CLICK HERE.

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