Taking into account all the current technological advances and system variables, the typical absolute accuracy that can be expected from a lightweight LIDAR system on a fixed-wing drone is approximately 10 cm (4 inches) horizontally and 5 cm (2 inches) vertically. There's an ongoing conversation in the unmanned aerial vehicle (LiDAR) community about accuracy, what it means and how important it really is. As with any technical question, this depends on what you want to achieve. Gert Riemersma, founder, president and technical director of Routescene, explains the basics of LiDAR accuracy and how to make sense of it all.
Accuracy can be defined as how close a measurement is to its actual value. Precision is normally expressed as an interval i.e. To determine the accuracy of the LiDAR, you'll need to perform multiple sensor measurements at the same point and then compare those measurements with those in the real world. Measurement in the real world is normally determined by using a much more precise measurement technique. A distinction must be made between relative precision and absolute precision.
Which one is of most interest depends on your request. For any survey, you can always determine the relative accuracy and, if in addition, your survey data must be analyzed with other data sets, absolute precision will be required. The relative accuracy of a sensor can be determined, for example, by comparing distances, such as the length of a roof in a 3D model, with the actual measurement of that ceiling and then calculating the variance of all measurements. This is usually done by plotting the results on a histogram. Absolute accuracy is defined as the difference between the position of a point in a 3D model and its real position in the real world.
Absolute accuracy takes into account the coordinate system in use, the transformation parameters and the errors present in the reference base station. The lower the number of errors and the smaller they are, the more accurate the system will be. When gross and systematic errors are eliminated, by checking the measurements, ensuring that there are no errors when annotating configuration information and performing a robust and rigorous calibration of the sensor, only random errors will remain. As the word indicates, these errors are random and cannot be modified, and this provides the quantitative value for accuracy. These random errors are usually represented graphically in a histogram and the variance of the results (that is, The distribution (of the measurements) will provide the measure of precision, which is normally expressed in terms of standard deviation or precision. The accuracy of drone LiDAR, improved with ground control objectives from Routescene Hardware, is only half the story.
We have dedicated our energies to the continuous development of our software, workflows and procedures, so that you can enjoy greater accuracy thanks to field calibrations to refine data collection with specific post-processing tools. Routescene is based on continuous improvement. We are constantly carrying out research activities (& of development) to achieve a deeper understanding of the errors inherent in all LiDAR systems. With this understanding, we strive to minimize errors through improvements in hardware, firmware and software. The ultimate goal: to produce more accurate systems that ultimately provide you with even better results.
Implementing land control using Targets improves confidence in results by providing an accurate reference point for comparison when measuring data points collected by UAVs LiDAR technology. This technology offers several advantages over alternative techniques such as photogrammetry or terrestrial laser scanning (TLS), including cost-effectiveness, speed of data collection and flexibility. Data processing, choice, quality assurance, trust in our products, and comprehensive training and support. The Routescene team is here to help: to discuss your requirements or to provide advice and customer support. With the reduction of information loss in tree crowns, UAV LiDAR can be used to extract structural and functional features from grasslands with an accuracy comparable to that of TLS.
Among five traits of grasslands studied - aerial biomass was least influenced by loss of LiDAR information from UAVs. A dissenting voice is that of Wingtra - a manufacturer of unmanned aerial vehicles for vertical takeoff and landing for professionals in cartographic, topographic and mining industries - which has decided not to use LiDAR based on unmanned aerial vehicles for topography. The worlds of unmanned aerial vehicles (UAVs), LiDAR and topography overlap; UAV LiDAR can shed light on places that are difficult or dangerous to access by other means. The loss of LiDAR information from UAVs in upper part of canopy had much greater influence on accuracy of estimating structural & functional traits of grasslands than lower part of canopy.
Average loss of height of UAV LiDAR at top of canopies exceeded 0.30 m & average relative height loss exceeded 49%, compared to 0.03 m & 6% at bottom of canopies respectively. However; grassland ecosystems are more likely to be influenced by loss of UAV LiDAR caused by dense vegetation canopies. Describe how implementing land control using Targets improves confidence in results; describe UAV's LiDAR technology; how it compares with alternative techniques; benefits & best use cases; data processing; choice; quality assurance; trust in our products; comprehensive training & support - Routescene team is here to help: discuss requirements or provide advice & customer support.