Your guide to the LIDAR drone landscape: finding the perfect fit

Illustration of a WingtraOne GEN II with Wingtra LIDAR flying over a landscape with power lines and a forest.

Something major has been happening over the past couple of years in the world of LIDAR data capture. Where before you had to hire a manned aircraft, now drones with LIDAR are changing the game.

This breakthrough happened as LIDAR sensors became light enough. LIDAR drone costs have started to push overhead around the data down dramatically. 

But how much you save—not just in raw cost of a system, but also in time correcting data and re-flying—depends on how good your system is. This article is written to inform you so you can make the right choice when you choose a system.

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Wingtra LIDAR sensor with monitor previewing LIDAR outputs

What is a LIDAR drone?

A LIDAR drone involves a LIDAR scanner that shoots millions of laser light pulses to the ground below its flight path. It receives the pulse information that bounces off the surfaces below—hard ground, leaves, branches, infrastructure. The sensor calculates the timing of all the bounces to read the distance the pulse travelled. This data offers precise horizontal and vertical insights about the surface below the flight area.

LIDAR is useful in cases of lightly to densely vegetated survey areas, because the pulses can reach the ground just like light would from the sky. This is in contrast to photogrammetry, where a photograph cannot capture ground-level detail in the case of vegetation, shadows or intensely homogeneous environments, like large areas of unmarked asphalt or large areas of sheer white snow with no features.

As you can imagine, LIDAR drones rely on a lot of hardware and software orchestration to deliver high quality results. Beyond this, processing data from a drone-mounted LIDAR sensor has until recently required extra know-how. So this article is designed to help you know what systems will get you the best results with the least learning curve, and why. Read on.

Leo Liu, Director of Mapping Solutions at Inertial Labs talks about LIDAR data capture and the importance of demystifying specs and checking for high quality in a solution.

Understanding LIDAR sensor technology

Before you can make an informed decision about what LIDAR drone system is best for your projects, you need to understand how it works. This way, you can assess specs and performance for yourself. 

A LIDAR drone represents an active sensing method. To know what that means, compare it to a photogrammetry drone, which carries a passive sensor; it flies and the camera shutter opens and closes to passively capture light information in the form of pixels that form photographs. A LIDAR scanner actively sends light pulses—by the hundreds per square meter— out and captures them, recording how fast they return to the sensor.

Illustration with the lidar drone components broken down and labeled
The optical module on a laser scanner contains components to send and receive laser light pulses; here is a more recent mock-up of where the tech is today.

A sensor that does all this with precision and accuracy will require some tightly coordinated hardware. In fact, the three critical components to a LIDAR drone sensor are the LIDAR laser scanner, which sends the light pulses, the inertial measurement unit, which measures the force and rate of movement of the sensor, and a GNSS unit, to tie the information to actual geolocations on the ground. 

The quality of these components and how tightly they are engineered will make a difference in the quality of the data you collect. This is especially true with LIDAR for drones, because the speed at which drones travel combined with the distance from the ground introduces more challenges to capturing accurate data.

Let’s look at each component and consider the quality range:

The laser scanner

You can think of this as the part that is responsible for sending and receiving the light pulses. We can look at the quality of this component according some key factors: 

  1. Wavelength and power. A high quality laser scanner will procure a stable and precise wavelength at a higher power so that the range is longer and it’s more resilient in conditions, like fog, rain or bright sunlight. Lower quality scanners have less stable wavelengths and less power, making the performance less dependable.
  2. Beam divergence is how tight and focused the laser beam is over a distance, the lower the divergence, the better. Lower quality scanners have high divergence and a reduced ability to detect smaller features.
  3. Pulse repetition frequency is just as it sounds: the ability to send and receive more pulses in a given time. More is better and results in higher resolution results.
Close-up of the Hesai XT32/XT16
As a quality benchmark, Wingtra LIDAR features top-of-the range Hesai XT32M2X 32-Channel 360° Spinning Mid-Range Lidar laser scanner technology for consistent and reliable results.
  1. Quality optics minimize distortion and information loss for clearer and more accurate measurements.
  2. The receiver quality is key to whether or not the sensor can pick up weaker returns from a greater height above ground and amidst more vegetation.
  3. The actual scanning mechanism involves solid-state tech that operates consistently over many repetitions when it is high quality. Lower quality mechanisms may falter in their performance and produce inaccuracies, inconsistencies and even gaps in coverage.
  4. Calibration: a high-quality scanner is regularly calibrated and maintains that calibration over time for consistency. Lower quality scanners can drift out of calibration, which impacts reliability of the data.

Wingtra’s Chief Technical Officer, Armin Ambūhl, describes the meticulous process of choosing the right components for the right reason at the right time.

The inertial measurement unit (IMU)

This is the part of the LIDAR sensor that tracks where it is in space and time so that the results captured by the scanner can be tracked accordingly. The better the IMU quality, the more aligned your results are and less work you need to do to correct them after a flight (see strip alignment info box below). To assess quality, you’ll want to consider five key factors here: 

  1. A high-quality IMU measures angular rates and accelerations with high accuracy and precision due to superior tech and advanced calibration processes. This results in lower drift rates which minimizes errors. In contrast, a lower-quality IMU will be prone more drift as well as errors and noise that reduce its reliability.
  2. Better IMUs rely on better components, including advanced gyroscopes and accelerometers that have better temperature stability and higher sensitivity. They may incorporate magnetometers to bring the performance up even more. Lower quality IMUs are prone to temperature changes resulting in poorer performance.
  1. Sampling rates correlate directly with quality: higher sampling means more frequent data collection and more detail, which is critical in such a dynamic active sensor environment where the drone is moving fast. 
  2. Noise filtering is key to the function of an IMU as the sensors own activity and external interference must be identified and disqualified from the data. Lower-quality sensors may struggle to filter this noise, which will introduce it to the results, which will have to be cleaned in post-process to be useful.
Wingtra employee inserting the Wingtra LIDAR into the WingtraOne Gen II at GeoWeek
Wingtra LIDAR incorporates an IMU by Inertial Labs, which can be credited with unprecedented strip alignment directly post flight for a LIDAR drone.

GNSS receiver

For a drone LIDAR drone survey, GNSS receiver plays a crucial role in determining the drone’s position relative to Earth. This information is factored into the information about pulse transmission and receipt. Here are five key factors that distinguish a high-quality GNSS receiver from a lower-quality one in drones: 

  1. The accuracy and precision of your GNSS receiver correlates directly with quality level. Higher quality receivers support multi-frequency bands and all major satellite systems, i.e. GPS, GLONASS, Galileo and BeiDou. So position accuracy is down to centimeter-level.
  2. Signal acquisition is faster in higher-quality receivers, even in challenging environments like canyons, dense forest and around tall infrastructure, so the lock is strong and the performance is consistent. With LIDAR drone data capture, you don’t want to lose lock because of a low-end receiver as it throws all the positioning data for that unlocked period into question.
Capturing precise and well-aligned LIDAR data for the output is part of the story, yet to use this data on a project and in automation applications, the data needs to sync precisely with real coordinates. That is what a good GNSS unit ensures, every time.
  1. Tech to minimize interference and signal jamming is a standard feature of a high-end GNSS receiver so that performance is more reliable in places where these factors will pop up. Interference affecting lower-end receivers can reduce their ability to provide accurate positioning data.
  2. Position update rate is higher in better-quality receivers. This is important for a LIDAR drone, where the aircraft is moving fast, and the data needs to be tied to a location as frequently as possible.

Beyond these specifics, all components need to be robust and durable in environments that present humidity, vibration and temperature extremes. This ensures dependable performance over a long sensor lifetime. 

Where LIDAR drones really shine and why

The three biggest applications where you can use a LIDAR drone for surveying today are: greenfields, powerlines and forestry.

Greenfields is a term used to describe vegetated lands that have not been developed. These kinds of surveys are usually performed for mining and construction pre-planning purposes to gauge the topography. Specifically, during mining exploration, information on the topography enables effective design of the area to be mined and managed.

In construction, topographical information helps to plan earthworks for both large and small infrastructure projects—from an urban block, to a solar farm to a highway project. Topographical LIDAR drone data in greenfields enables accurate planning and more precise allocation of resources based on realistic assessments of what must be moved.

Screenshot of a LIDAR outputted map of a forest with phoenic lidar systems software
A forest mapping dataset captured with Wingtra LIDAR, available for inspection here.

In mining, LIDAR drones help in exploration and pre-planning, as well as planning earthworks on active sites. Managers get a view of the terrain beneath vegetation to plan the excavation and create work estimates for the development of the pit or initiation of stripping. Likewise, returning the land to its original state is feasible with this precise preview of the Greenfield. 

Powerline mapping is often done to analyze vegetation proximity and potential encroachment as well as the condition and position of the powerlines themselves. Engineers use this data to check for potential hazards like overgrown trees or structural damage. Early detection means scheduling efficient repair and prevention of fires and power outages.

Forest mapping is for obvious reasons a great application for LIDAR drones, which not only deliver information about the ground beneath vegetation, but also about the crown heights as well as number of trees in a given area.

Graph showing the popular brands in the LIDAR landscape today, with price on the x axis and accuracy and efficiency on the y axis
An at-a-glance look at the popular brands on the drone LIDAR landscape today with quality and price range in focus.

Common myths dispelled

Because of the complexity of LIDAR drone data capture, it takes time to really understand how it works and what its strengths and limitations are in a range of real-world circumstances. This leaves a lot of meantime for misconceptions to pop up based on a limited understanding. Here are some common ones:

Myth: LIDAR sensors penetrate forest canopy

While it is tempting to think that—since LIDAR data can detail the ground beneath certain densities of vegetation—it can go through physical objects, it cannot. What LIDAR relies on is the penetration of light through small open spaces that allow for it to reach the ground. Rule of thumb: If you can stand in a forest, look up and see sky or light around the leaves of the canopy, you will be able to capture ground-level information from pulses of light sent from above. There is a level of canopy thickness that even the highest-end LIDAR cannot deliver ground information about.

Myth: LIDAR is always better than photogrammetry

In some cases, LIDAR will outperform photogrammetry, just like in some cases photogrammetry will outperform LIDAR. The difference is what information you need and why. If you need terrestrial information from a greenfield with moderate vegetation, LIDAR drone capture will definitely work better. If you need a detailed orthomosaic with photorealistic, high-resolution looks at the ground and features in a certain area that is not very vegetated, photogrammetry will deliver what you need. 

For more insights between the two, you can read our in-depth comparison on lidar vs. photogrammetry.

Myth: More returns mean better outcomes

In the world of drone LIDAR this has simply not been proven to be true. If you understand what a return is and how it fits into the context of a drone survey, the reason becomes evident. In short, drone LIDAR involves more pulses per square meter than airborne LIDAR. A return rate is the number of signals that bounce back from just one pulse. If you have three strong returns and two faint ones, you have noise in your data. Meanwhile you have enough pulses to get sufficient information. Check out this visual for a more complete picture:

How much is a LIDAR drone?  

Drone LIDAR systems can range from USD 13K to USD 150K (see levels of quality graph and video above). To answer this question in a meaningful way, however, you need to ask also how much time and training it saves. This question will highlight the actual return on investment figure, which is really the true benefit of a system.

Factors that can bring hidden costs to a cheaper LIDAR drone solution include: 

  • Excess field time for setup and calibration due to complicated system with lower-end components
  • Extra time for strip alignment after every flight
  • Reflight; excessive training on setup and/or processing
  • Complexities that prevent repeatability across pilots
Graphic with the WingtraOne Gen II and a Wingtra LIDAR, with various text boxes mentioning the main bonuses about having a high-quality LIDAR sensor
The old adage “cheap can be expensive” is one that Wingtra continues to remind buyers about. You can save on a quick purchase of a cheaper drone, but calibration can eat into flight time, strip alignment can slow down big jobs. The hassles and inefficiency of a system over several years then can cost way more than you saved purchasing the platform.

Beyond photogrammetry’s reach: why LIDAR drones matter

If it isn’t crystal clear, LIDAR is a way to get precise and accurate ground information in the midst of vegetation. While photogrammetry is disrupting many industries with its ability to provide precise and efficient aerial data capture, it simply cannot offer this needed terrain information. 

It wasn’t five years ago that you had to hire an airplane and spend tens of thousands to conduct a broad-ranging LIDAR survey. This cost was prohibitive to doing it as often as truly needed in many cases, such as vegetation encroachment on powerlines or forest monitoring surveys.

Other exploration projects could also be delayed by this prohibitive cost when planning a business budget. With drone LIDAR, the cost is coming down, and the doors of progress are opening for those stepping forward to use it.

Now is the time to invest in a LIDAR drone

As the cost of LIDAR data comes down more gradually than the tech evolves, contractors can make good money to provide LIDAR drone data, and large corporations can bring drone survey units in-house. Thanks to Wingtra LIDAR, Wingtra Support and a constant focus on refining reliability as the bar of quality rises, training and integration has become smoother than ever around mid-range systems.

Graph showing the cost changes with developing LIDAR technology and where we stand today
Drone LIDAR is changing the landscape around what can be charged for a LIDAR drone survey, but there is a lag in the market. The following three to five years present income opportunities for service providers especially—provided their data is captured and processed with the lowest possible overhead.

Game-changing applications and research with LIDAR drones

Research into UAV LIDAR is key to adoption, since this is where the technical advances driven by the market are tested outside of it. The neutrality and objectivity of the findings clear out the marketing noise and give grounded insight around the use of different kinds of systems across a range of use cases. Here are some examples. 

Powerlines involve thousands of parts, such as wires, ground wires, insulators, clamps, and dampers. Natural disasters like windstorms, lightning, and fires, or human influences, like pollution leaks, damage these parts, resulting in overheating, erosion, or fires. Overgrown trees under powerlines can also cause short circuits.

Since they are such a massive stretch of infrastructure, the automation of their monitoring—based on accurate and frequent LIDAR drone data— would be a huge boon in terms of risk prevention. I.e., power outages and fires could be prevented at a much higher rate, meaning economic and environmental protection.

Scanned point cloud of the sample segment of powerline corridor
An output from research entitled: “Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne LiDAR,” which highlighted massive efficiency boosting potential of drone LIDAR when performing vegetation encroachment surveys.

In terms of archaeological studies, the development of drone LIDAR is seen as a democratization in that some studies simply were not possible when the costs of aerial or ground LIDAR were so high. Now, it is possible to research in forested areas and look into the past on a research budget. 

Large scale agriculture stands to benefit greatly as well from drone LIDAR, since land levelling is more efficient and effective based on accurate data to begin with. Specifically, levelling land ensures water distribution and sustainable, economical practices for field maintenance.

Mining applications for drone mounted LIDAR include pre-planning surveys of greenfields as well as planning surveys on active sites. The research linked highlights a year-long exploration of how LIDAR drone data aided in 3D structural modeling for stope designs and how this data compares to traditionally sourced data used in the past.

Leading entry-mid range systems at a glance

No calibration
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Automation for repeatable flights
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Minimal training needed to collect and process data
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Strips aligned upon landing
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Efficient data capture over large areas
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Ability to hover and collect vertical assets
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In the end

If you’re serious about UAV LIDAR, keep learning, get second opinions and feel free to reach out to us for a detailed conversation about the advantages of Wingtra LIDAR.

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