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What is a LiDAR Payload?

Friday, 5 January, 2024


What is a LiDAR Payload?

LiDAR, short for Light Detection and Ranging, has gained significant traction in the past decade as a cutting-edge technology. Its application extends from NASA and meteorology to now being integrated into smartphones like the iPhone 12 Pro. LiDAR technology finds widespread use in surveying, mapping, and inspection, delivering meticulous and precise point clouds of any environment.

These point clouds, typically comprising hundreds of thousands of scanned points per second, coalesce into a vivid 3-dimensional representation of the surroundings, akin to a pointillist painting. LiDAR scanners excel in utilizing light pulses to remarkably determine object distances. However, on its own, LiDAR solely gauges relative ranges without spatial positioning awareness.

Figure 1. Example Pointillism Painting (https://artincontext.org/pointillism/

A comprehensive LiDAR payload typically includes a GNSS-aided Inertial Navigation System (INS) in conjunction with a LiDAR scanner. This integration is pivotal for georeferencing, a process that converts LiDAR’s relative data into individual geographic coordinates. By continuously recording orientation, position, velocity, and timing (OPVT) in a global reference frame, the GNSS-aided INS complements LiDAR ranging data.

Combining this recorded data with LiDAR’s ranging information generates a directly georeferenced point cloud. This powerful dataset delineates the absolute location of every point within the point cloud, constituting what’s termed as geospatial data.



LiDAR emerges as an indispensable tool for precision-driven mapping endeavors across diverse industries such as archeology, agriculture, construction, and conservation. Leveraging drone-based LiDAR technology offers the safest, most precise, and expeditious approach to collecting environmental data.

Drone-based LiDAR empowers users to swiftly generate highly accurate three-dimensional models encompassing vast areas. An exemplary application lies in monitoring construction site progression. By harnessing Drone LiDAR, the arduous task of terrestrial surveying across expansive construction sites becomes redundant. Instead, the geospatial insights gleaned from LiDAR enable construction teams to efficiently track building advancements, comparing them against original schematics. [1]


Apart from mapping, LiDAR serves as a crucial tool for precise inspections, a necessity for its practical application. A prime example requiring exacting precision is the inspection of transmission lines. As the global power grid expands, the proliferation of long-distance transmission lines becomes inevitable. Often spanning hazardous or remote terrains, these lines demand continual monitoring for maintenance and asset inventory.

The limitations of terrestrial-based observation methods are apparent in meeting the operational demands of these extensive grids. Drone LiDAR emerges as a game-changer, enabling users to cover lengthy transmission lines exponentially faster than ground-based survey teams.

Moreover, LiDAR’s capability of multiple returns proves invaluable. It allows users to comprehensively map power lines, underlying terrain, and surrounding vegetation in a single scan, significantly enhancing inspection efficiency. This becomes pivotal in monitoring vegetation around power lines, mitigating potential hazards such as overhanging foliage that could disrupt power transmission. [2]


Surveying stands as a critical application underscoring the paramount importance of precise data collection across various discussed sectors. Its demand for high accuracy extends to delineating property lines, establishing building locations, charting road topography, and more. Precision in surveying often translates to an absolute accuracy of 10 centimeters, known as survey-grade accuracy.

Traditional land surveying employs ground teams utilizing theodolites to ascertain vertical and horizontal angles between points. While a reliable method, it’s not without drawbacks. These surveys entail considerable time investment, especially for covering extensive land areas. Moreover, navigating potentially hazardous terrains poses significant risks to ground survey teams. [3]

The advent of drone-based LiDAR systems has revolutionized land surveying by leveraging advanced capabilities. Drones offer unparalleled ease in topography measurement, granting access to expansive land areas, thereby eradicating the need for guesswork or inference.

Notably, data capture is significantly expedited using drones compared to traditional land survey teams, requiring fewer human resources for completion. The efficiency of drone LiDAR systems also translates to reduced long-term costs, as data acquisition occurs swiftly and more efficiently than with ground-based methods. [4]

Actionable Data

Point Cloud Classification

Point clouds serve as a foundation for numerous essential deliverables in mapping and inspection projects. Point cloud classification involves assigning predefined labels to point groups, defining their association with specific objects within the cloud. A key instance of this classification lies in identifying ground points within the point cloud.

LiDAR scanners, often equipped with multiple returns, possess the ability to penetrate obstacles such as vegetation, enabling access to ground surfaces. This classification of ground points proves instrumental, empowering users to create invaluable deliverables tailored to meet their customers’ needs.


A prominent deliverable stemming from point cloud classification is the creation of a digital elevation model (DEM). Utilizing solely the classified ground points, a DEM offers a refined bare earth elevation model, crucial for various applications like hydrologic modeling, terrain stability analysis, and soil mapping.

In hydrology, DEMs play a pivotal role, aiding in coastal hydrologic modeling by delineating watersheds and facilitating calculations for flow accumulation and direction. These models are instrumental in predicting landslides and ensuring terrain stability, as demonstrated in the accompanying figure. [5]

Figure 2. DEM Example (Slope Stability & LiDAR — Terrainworks)

Hillshade Models

Hillshade models represent a crucial deliverable, offering a standard method to unveil topographic features that might otherwise remain concealed. Designed for functional clarity rather than aesthetic appeal, these models prioritize maximum contrast to reveal intricate terrain characteristics. Typically derived from Digital Elevation Models (DEMs) or Digital Surface Models (DSMs), hillshades simulate a hypothetical light source illuminating the elevation surface.

These models serve diverse purposes, often serving as background visuals for cartographic maps and proving instrumental in identifying and predicting terrain hazards like landslides. Their capacity to detect subtle terrain variations is invaluable in assessing topographic features. [6]

Figure 3. Example Hillshade Model (https://www.esri.com/esri-news/arcuser/fall-2014/multi-directional-hillshade-makes-your-maps-pop)

WISPR Systems– The Complete Solution Provider

Established in 2016, WISPR Systems operates as a Mississippi-based manufacturer, specializing in robust and adaptable commercial drones along with an array of payload options. Renowned for reliability, WISPR Systems collaborates with Inertial Labs to furnish end-users with holistic surveying solutions spanning drones, LiDAR, photogrammetry, and GIS-centric deliverables, catering to diverse industries and applications.

Distinguished by more than just product provision, WISPR offers integrated solutions, accompanying users with requisite training to derive valuable and actionable insights. Leveraging extensive expertise in remote sensing, WISPR seamlessly navigates users through comprehensive workflows, serving as a single-source provider for complete remote sensing solutions—from meticulous flight planning and data capture to meticulous processing and final deliverables.

This setup underscores WISPR’s commitment to delivering a seamless experience, empowering users to harness the full potential of remote sensing technology for their specific needs.

Inertial Labs RESEPI XT-32 vs. M2X

Testing Process

Inertial Labs and WISPR partnered to create a wholly integrated drone LiDAR solution using the Ranger Pro with the RESEPI XT-32 and RESEPI M2X. The drone and the navigation system of each complete solution remained the same; the only change was the LiDAR scanner. Below is a comparison of both scanners in a few key parameters.


Table 1. XT-32 vs M2X Specification Comparison

WISPR sought to test the performance of both systems by flying the same route at three different AGLs: 50 meters, 100 meters, and 150 meters. For reference, the image below shows the comparison between the flight paths of the M2X and XT-32 at 50 meters AGL in Inertial Labs’ PCMasterPro Software Suite.

Figure 4. 50m AGL Flight Path XT-32 (left) vs. M2X (right)

An essential aspect of WISPR Systems’ methodology is the meticulously designed flight paths ensuring comprehensive, high-density coverage. Each path is strategically crafted with a 50% overlap, clearly depicted by color-coordinated passes and corresponding points. Notably, the scanner’s configuration utilizes a horizontal field of view set at 120 degrees, a crucial parameter influencing data collection.

Examining the manifestation of these parameters at varying Above Ground Levels (AGLs) proves pivotal. The figures below illustrate the flight path’s representation at both 100 meters AGL and 150 meters AGL, offering insights into how these specific settings impact data acquisition and coverage.

Figure 5. 100m AGL Flight Path XT-32 (left) vs. M2X (right)

Figure 6. 150m AGL Flight Path XT-32 (left) vs. M2X (right)

With fewer passes at a fixed 120-degree field-of-view, it is easier to see the overlap of each successive pass in the flight as the height AGL increases. With features like heavy vegetation, power lines, and buildings, this environment is a phenomenal place to examine many aspects of the performance of each payload.

Data Analysis and Comparison

The point clouds for all six flights are shown in the figure below, and it becomes clear that the AGL plays a significant role in the detail and density of a point cloud. These point clouds are colorized based on altitude and include all points from the respective flights.

Figure 7. M2X (top row) vs. XT-32 (bottom row) at 50, 100, and 150m AGL (from left to right) Full Point Cloud

From a macro perspective, we can see a few apparent differences between point clouds at different AGLs. As the AGL increases, structures requiring precision mapping, like power lines, lose details or are entirely omitted from the point cloud. A critical difference between the M2X and XT-32 comes from the 100m AGL cloud, where the roofs of the buildings begin to get cut off on the XT-32 but remain intact with the M2X.

Another critical parameter of a LiDAR scanner is its ability to penetrate vegetation, allowing users to get data points of the ground beneath the vegetation. As a result, surveyors can get detailed DEMs, hillshade models, and more, even if objects block the scanner’s line of sight. These models can highlight important terrestrial features not visually apparent to the naked eye. A great example of the power of LiDAR is highlighted in the figures below, which compare data at 100m AGL between the XT-32 and the M2X.

Figure 8. M2X (Left) vs. XT-32 (Right) 100m AGL – Point Cloud

Most of Figure 8 contains very heavy vegetation to the point that the ground beneath the trees in this point cloud is not visible. To see the ground for this entire view, let’s classify and display only the ground points from this cloud, effectively removing all points above the ground surface. Figure 9 below displays the ground-classified cloud.

Figure 9. M2X (Left) vs. XT-32 (Right) 100m AGL – Ground Points

Despite heavy vegetation, these scanners performed exceptionally well to penetrate the ground, even at 100m AGL. Upon inspection, both scanners are highly comparable in this facet, with the M2X having an edge in point density. They also allow us to get a surface model of the cloud in this view, as shown in Figure 10 below.

Figure 10. M2X (Left) vs. XT-32 (Right) 100m AGL – Surface Model

This surface is especially impressive because it uncovered this small ravine running through the center of the landscape that was not visually apparent without using this model. These impressive ground classified clouds were not specific to this view and AGL; the figures below display this capability of both scanners over varying scenarios.

Figure 11. M2X (Left) vs. XT-32 (Right) 150m AGL – Point Cloud

Figure 12. M2X (Left) vs. XT-32 (Right) 150m AGL – Ground Points

Figure 13. M2X (Left) vs. XT-32 (Right) 150m AGL – Surface Model

The example above covers a similar area but at an AGL of 150 meters. This height starts to strain the range of the XT-32, and it shows when we look at the point density of the ground points and surface model. The M2X produces a much more detailed solution in this instance, which makes sense as the range specification of the M2X is 300m and the XT-32 specification is 120m.

We can further illuminate features by deriving a hillshade from the DSMs found in the previous figures. The flight scanner and the AGL can play a role in the detail and accuracy of the hillshade model. The figure below compares the same hillsides at varying AGLs for each scanner.

Figure 14. M2X (top row) vs XT-32 (bottom row) at 50, 100, and 150m AGL (from left to right) Hillshades

As shown above, the M2X produces detailed, clean hillshade models up to 150-meter AGLs. The XT-32 also produces clean hillshade models within its specified range, but once the AGL reaches above 150 meters, we see that part of the data is cut off. Additionally, both scanners allow the user to identify a ravine in this terrain despite it not being visible to the naked eye or on the raw point cloud data. This is partly due to the nature of hillsides, as they are high-contrast models.

In the remote sensing industry, many different terms get thrown around about accuracy. In general, any accuracy specification can fall under either relative accuracy or absolute accuracy. In the context of a complete LiDAR payload, relative accuracy measures the accuracy between points relative to each other within a single project. Absolute accuracy measures how close a measured value is to a known, surveyed location (actual value) in a geographic coordinate system. An example of how relative accuracy and absolute accuracy are measured is shown in the figure below. This is an important distinction when analyzing the quality of LiDAR remote sensing solutions, as these values speak to the quality of different components of the remote sensing payload. Relative accuracy is more dependent on the LiDAR scanner, while absolute accuracy is more dependent on the quality of the inertial navigation systems (INS) on board. Both parameters are essential for surveying applications to get the job done correctly.

Figure 15. Relative vs Absolute Accuracy

The first accuracy parameter that was examined was relative vertical accuracy. This was done by taking data from 2 parallel passes and comparing a slice of the overlapping section. Datapoints are colored based on selection, so what points came from what selection will be evident. An example of this parallel pass method at 50 meters AGL is shown in the figure below.

Figure 16. Parallel Passes at 50 meters AGL

All data used for relative accuracy measurements were collected on flat ground. Data was collected and compared at 50 meters AGL, 100 meters AGL, and 150 meters AGL.

Figure 17. M2X (Top) vs XT-32 (bottom) 50m AGL Relative Accuracy Comparison

Figure 18. M2X (Top) vs XT-32 (bottom) 100m AGL Relative Accuracy Comparison

Figure 19. M2X (Top) vs XT-32 (bottom) 150m AGL Relative Accuracy Comparison

As shown in the above figures, it is evident that the M2X and XT-32 perform almost identically in relative accuracy for 100 meters AGL or less comparisons. Both devices have close to 3 cm relative accuracy at 50 meters AGL and close to 6 cm relative accuracy at 100 meters AGL. We see that at 150 meters, the M2X has a more significant point density than the XT-32 and has a relative accuracy of about 9 cm, while the relative accuracy of the XT-32 is closer to 10 cm.

The next step is to check the vertical absolute accuracy of the system. This was done using ground control points (GCPs), surveyed points of a known location. This environment contained 7 GCPs, and in the Lidar360 point cloud analysis software, the LiDAR points from both scanners were compared to the surveyed locations. The de-bias does not delete, add, or change the relative position of the points; it is only a systematic adjustment/shift of the entire point cloud based on observed/detected systematic offset. In this instance, a manual shift of 0.151 feet (~4.6cm) was applied to the cloud. Table 2 below compares the RESEPI M2X and RESEPI XT-32 in crucial metrics of absolute accuracy at an altitude of 60 meters.

Table 2. Absolute Accuracy Comparison

The table above shows that both payloads had impressive absolute accuracies after debiasing with a root mean square (RMS) accuracy of less than 2 cm. It is readily apparent that all absolute accuracy metrics are well within the survey-grade benchmark of 10 cm. Most of them are within 1/5th of the required threshold. The similarity of both payloads’ absolute accuracy points to the repeatability of Inertial Labs’ inertial navigation systems, as both absolute accuracies are nearly identical. Regardless of the scanner, both devices can give surveyors immense confidence in their results after they survey an environment.


LiDAR is a powerful technology that allows users to capture an environment in detail and with high accuracy while saving man-hours and reducing the safety risk of the survey team. LiDAR payloads can produce insightful deliverables such as DEMs, DSMs, and hillshade models that give the end user actionable results. WISPR systems provide a wholly integrated drone-LiDAR system and have the expertise to support users in getting the most out of their data.

The RESEPI-M2X is the higher-end model of the scanner comparison, and it is understandable when looking at head-on comparisons at higher AGL heights. It provides dense models and impressive vegetation penetration, allowing users to fly at various AGLs while maintaining a high-quality data precision and density standard. The XT-32 proved to be a fierce competitor to the M2X, especially at AGL heights of 100 meters or less. The XT-32 is a cost-effective model that can still provide dense, accurate point clouds of many environments, penetrate vegetation, and provide accurate, actionable deliverables to customers.

Ultimately, the suitable scanner for the end user depends on several factors: budget, environment, accuracy requirements, etc. Both scanners could produce accurate dense clouds, penetrate heavy vegetation, and generate insightful DSMs, DEMs, hillshades, and more. For applications at lower AGL, such as asset inspection, the XT-32 may be the right choice to get detailed imagery of essential resources. If a user needs to map out a large stretch of land, then the M2X could allow the user to gather data from a higher AGL while maintaining adequate accuracy and density for many mapping projects. No matter what, users can’t go wrong when trusting WISPR systems to provide a complete drone-LiDAR system that reliably produces accurate, actionable data in a user-friendly fashion.


[1] “A Guide To Using Drones and LiDAR Technology for GIS Mapping,” www.duncan-parnell.com. https://www.duncan-parnell.com/blog/using-drones-for-gis-mapping/

[2] Benowitz, “LiDAR Equipped UAVs,” enterprise-insights.dji.com, Jul. 28, 2022. https://enterprise-insights.dji.com/blog/lidar-equipped-uavs

[3] 2022 “Land Surveying: The Process and the Tools,” EngineerSupply. https://www.engineersupply.com/land-surveying.aspx

[4] “How Have LiDAR Drones Transformed Traditional Land Surveying?,” blog.smartdrone.us, Sep. 05, 2022. https://blog.smartdrone.us/insights/how-have-lidar-drones-transformed-traditional-land-surveying#:~:text=LiDAR%20drones%20enable%20drone%20surveyors%20to%20map%20the (accessed Dec. 08, 2023).

[5] GISGeography, “DEM, DSM & DTM Differences – A Look at Elevation Models in GIS,” GIS Geography, Mar. 09, 2016. https://gisgeography.com/dem-dsm-dtm-differences/

[6} 2018 Čučković, “Some thoughts on hillshade models for Lidar analysis,” landscapearchaeology.org, Nov. 10, 2018. https://landscapearchaeology.org/2018/lidar-hillshade/ (accessed Dec. 08, 2023).

For more information:
Anton Barabashov
VP of Business Development
Inertial Labs Inc.

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