LiDAR technology, whose name stands for Light Detection and Ranging, has turned upside down how the real, physical world is captured and analyzed. This leading-edge remote-sensing method uses laser light to take measurements and produce extremely precise and accurate 3D renditions of the natural and human-built world. From managing forests to guiding autonomous vehicles, LiDAR systems have become indispensable tools across many sectors of the economy, and for good reason: They have high precision, they are versatile, and the cost of using them is coming down.
LiDAR technology operates on some basic principles that allow it to serve various professionals efficiently in gathering intricate spatial data. These data span large areas and cover many levels in space (geometrical, vertical, and horizontal). They serve acts like mapping surfaces and profiling steep geological features, and they are used in the otherwise concealed parts of space (virtual tunnels, etc.). LiDAR data also support acts that deal with time (using data in real-time or serving as ‘ old news’ in acts like monitoring forest health). And they are involved as essential players in acts that bring the unfolding of space closer to human reach in both near- and far-off future timeframes.
This detailed tutorial deals with the foundational principles of LiDAR, how LiDAR systems work, the ways of LiDAR data collection, the method of point cloud generation, and the factors affecting measurement accuracy. If you’re a professional thinking of using LiDAR in your work or are just interested in this transformative technology, this article will give you a solid understanding of the LiDAR fundamentals.
Table of Contents
Core Principles and How LiDAR Works
What is Light Detection and Ranging?
LiDAR (Light Detection and Ranging) is a remote sensing technique that employs light in the form of a pulsed laser to gauge the variable distances to objects on the Earth’s surface and in the atmosphere. These light pulses, along with other data recorded by the airborne, terrestrial, or satellite-based LiDAR system, combine to provide a very accurate, three-dimensional picture of the target object’s shape and certain distinguishing characteristics—in other words, a precise mapping of the object’s surface.
LiDAR technology functions on a straightforward but potent principle. By sending out laser pulses and timing how long it takes for the reflected light to bounce back to the LiDAR sensor, systems that use LiDAR can figure out how far away something is with impressive exactitude. Whereas radar sends out radio waves and sonar sends out ultrasonic waves, LiDAR uses light waves in the near-invisible part of the spectrum.
LiDAR technology is versatile because it can penetrate vegetation and work in many different lighting conditions, all while capturing millions of measurement points in a short time. These various capabilities make it an invaluable tool for applications that range from topographic LiDAR mapping to autonomous navigation, where detailed spatial awareness is critical.
The Basic Measurement Principle
The fundamental operating principle of LiDAR is simple: determine how long it takes for a laser pulse to go to a target, bounce off it, and return to the LiDAR sensor. Light travels at a known constant speed, and since half of the pulse’s journey is the distance from the sensor to the target and back, the simple calculation of speed times time tells you how far away the target is. Of course, there are tweaks for using this to make topographic maps and models, but that’s the idea.
Dividing the total time by two accounts for the fact that the total time includes both the journey to the target and the return trip to the sensor. This time-of-flight principle forms the foundation of all LiDAR systems. Of course, various enhancements and additional technologies increase the capability of a modern LiDAR system, such as the ability to emit hundreds of thousands to millions of laser pulses per second, with each pulse providing a precise distance measurement to the target. When all of these individual measurements are combined with the exact position and orientation of the LiDAR sensor at the time each pulse was emitted, the result is a fairly comprehensive three-dimensional representation of the environment.
These distance measurements depend on several factors for their accuracy, such as how precise the timing mechanism is, how good the optical sensors are, and the environmental conditions. The best (i.e., most expensive) LiDAR systems can measure distance with an accuracy of a few millimeters, which makes them applicable to areas that require extreme precision.
Evolution of LiDAR Technology
Since the 1960s, when it was first developed, LiDAR technology has advanced hugely. It began its early life largely in the area of meteorological and atmospheric scientific research. The laser was a new invention then, and this new use of light was seen as having great potential. But those first LiDAR systems had only a few limited applications, and they worked well only for a few specific purposes.
Topographic mapping began to use LiDAR in the 1970s and 1980s. It was, at that time, an elevation model building tool. And it moved to a higher plane, it seems, when we added the Global Positioning System (GPS) to the LiDAR aircraft in the 1990s. GPS was a game changer, and it’s hard to image how we could do these projects more accurately without it.
Decreasing costs and increasing capabilities during the 2000s led to the widespread adoption of LiDAR technologies across many sectors. User-friendly operational characteristics and advancing electronics enabled the integration of solid-state LiDAR into a new generation of consumer devices. By the 2010s, mobile devices and vehicles were using computers that could operate in real time with the kinds of data that LiDAR could deliver.
The modern LiDAR systems of today are renowned for their unparalleled resolution, range, and processing power; these traits have turned LiDAR from a specialized scientific tool into an absolutely vital technology with applications ranging from…
Essential Components and System Types
Key Hardware Components
LiDAR systems have several critical components that work together to capture spatial data, process it, and deliver it in an accurate form. Understanding these key LiDAR system parts is essential to understanding how LiDAR works; knowing what makes one part distinct from another helps us comprehend what might make certain LiDAR systems more or less suitable for different applications (atmospheric vs. terrestrial, for example). The laser emitter is the critical heart-of-the-matter component in any LiDAR system, generating the light pulses used for measurement. Most LiDAR systems use near-infrared laser light, approximately 900 to 1064 nanometers in wavelength (though the systems that do use these wavelengths span a fairly wide range of operational capabilities). Bathymetric LiDAR, in contrast, uses green light (532 nm); this is a more penetrative type of measurement instrument for underwater surfaces.
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The mechanisms for scanning direct laser pulses over the desired field of view. These mechanisms include:
- Mechanical systems that use rotating mirrors.
- Microelectromechanical Systems (MEMS) that have oscillating mirrors.
- Optical phased arrays for solid-state LiDAR.
- Flash LiDAR systems that illuminate the entire scene all at once.
Each mechanism offers a different set of trade-offs in terms of speed, reliability, field of view, and resolution that makes it better or worse for a specific context. For instance, using a rotating mirror might not make sense in a mobile application where the LiDAR unit is oscillating up and down or side to side.
Common System Types and Their Applications
Different measurement principles make LiDAR systems versatile for various applications. Time-of-Flight (ToF) LiDAR directly measures the time taken for a laser pulse to travel to a target and back. ToF offers range capabilities (up to several kilometers) making it ideal for aerial mapping, autonomous vehicles, and forestry applications. ToF employs a high pulse rate, necessitating a high-precision timing system capable of resolving time differences on the order of nanoseconds.
Phase-base LiDAR uses a continuous wave laser and measures the phase shift in the returned signal. This is what bifurcated LiDAR does at exceptionally close range and high precision in an industrial context. Bifurcated LiDAR employs an optical spectrometer to perform the phase shift measurements. I will return to this example of using phase shift measurements in the context of an industrial application.
These systems measure range with higher accuracy simply because the phase shift is a much better quantity to measure than the time of flight (ToF). They also have a much higher pulse repetition rate, which is key in sweeping across a large area to obtain high point densities (at least an order of magnitude in excess of what is obtained with ToF).
Real-World Data Collection Methods
Practical Approaches to Data Collection
LiDAR data collection methods vary widely depending on the application and environment. Understanding these different approaches helps in selecting the appropriate method for specific needs and maximizing the value of the collected data.
There are two main types of platforms from which LiDAR can be collected: airborne and terrestrial.
Airborne LiDAR systems can be mounted on various aircraft, including fixed-wing planes, rotary-wing helicopters, and drones. Airborne systems usually are operated at altitudes of 500-3000 meters. They have the advantage of covering large areas efficiently, making them ideal for things like topographic mapping (over a huge area), corridor mapping (for highways, power lines, etc.), flood modeling, and mapping forests. Point density (the number of points per unit area) with airborne systems varies tremendously. In the NOAA’s experience, point density in surveys has ranged from 1-20 points per square meter. The systems used by NOAA are not the only ones out there by any means, and the next bullet discusses systems operated by other entities. Still, I offer what I think is a usable range in terms of # of points per square meter, for folks who may be thinking about different aspects of LiDAR.
NOAA survey aircraft can be deployed for all kinds of coastal applications.
Cost-Benefit Considerations for LiDAR Implementation
When assessing which method of collecting LiDAR data to use, many economic and practical concerns must be tackled to guarantee that the winning method will provide the best value for the specific application. The problem begins with the investment in collecting equipment, which can vary greatly even with different types of terrestrial sensors. Basic terrestrial LIDAR systems start at around $15,000, yet basic, aerial drone systems can run anywhere from $40,000 to $150,000. And these are not aerial, helicopter, or airplane systems, which are even more expensive. At the high end, an aerial LIDAR system can cost over $300,000. That these system-level costs must be recouped in part across the use of several different methods and types of projects is obviously a major concern. And then, of course, there are the operational costs, which on a per-unit basis are obviously vital to factor into the equation. What must a collection methodology reasonably cost per day or hour? And what is included in that cost? And how do collection methods stack up against each other in this regard? On the low end, Drones can be operated for about $100 per day, but an aerial crew can easily cost upwards of $1,500 per day. And then, what is included on the high end and low end in terms of “value” for the specific application being plugged into each method?
A recent case study showed the measurable ROI of putting drone-based LiDAR to work for land surveying. The study showed a 60% reduction in field time and 40% overall cost savings compared to traditional methods. For companies doing two or more surveys per month, the payback period was just three months. This rapid transition from “cost center” to “competitive advantage” illustrates how quickly LiDAR can transform survey operations.
From Data to Actionable Information
Point Cloud Generation and Processing
The primary output of LiDAR data collection is a point cloud, a collection of data points in three-dimensional space that represents the external surfaces of objects. Point cloud generation from LiDAR data involves several key steps. Data acquisition entails providing adequate conditions for the LiDAR system and the positioning (e.g., GPS, GNSS, IMU) sensors to work optimally. For both static and mobile systems, the trajectory of the sensor is tracked throughout the data collection process. The system calculates the distance for each laser pulse based on either the time of flight or the phase shift of the returning sound or laser pulse, and it records the returns while simultaneously collecting positioning data. Once a point cloud is generated, various processing techniques transform it into valuable information products. The most basic technique is filtering, which uses different methods to control point errors and to classify points based on what they represent (ground, vegetation, buildings, etc.). More sophisticated techniques such as centimeter-level registration, which makes use of control points to achieve a high level of accuracy for the initial point cloud, have also been developed. These and other techniques have greatly enhanced the performance of LiDAR systems and the quality of point clouds for various analyses and applications.
Industry Applications with Proven ROI
Transformative LiDAR Applications Across Sectors
LiDAR technology has revolutionized operations across numerous industries by providing spatial data at an unprecedented level of detail. That makes it possible to make smarter decisions, run operations with increased efficiency, and do things that were just not possible before.
When it comes to the return on investment from implementing LiDAR, case studies and real-world applications speak most clearly. In the surveying and mapping sector, LiDAR has transformed traditional practices by moving those practices to a new level of efficiency and, most importantly, increased data quality.
Topographic mapping done with LiDAR creates highly accurate digital elevation models (DEMs) and contour maps in a fraction of the time required by conventional methods. Corridor mapping efficiently documents the linear infrastructure that crisscrosses our world—routes for transportation, pipelines, and transmission lines. It captures the kinds of data we need to make a single pass over the area and then covers the area comprehensively.
In reduced field time by 75% and overall project costs by 40%, a Canadian surveying firm using aerial scanning LIDAR on a development project that stretched over 200 acres demonstrated the tangible benefits of corridor mapping.
In the construction industry, and in the closely allied infrastructure sector, LiDAR has found ready acceptance for providing accurate as-built documentation and support for something called Building Information Modeling (BIM). Being able to give very precise as-built documentation of existing structures is important to these two industries and allows them to work with (and not against) geometry that is very complex.
In highly precise environments (up to 1,400 feet in the air), LiDAR can measure to within 1/10,000th of a second, enabling exact and stunningly detailed scans of the world around us. On a highway expansion project that cost $50 million, an early warning system called LiDAR identified potential design conflicts before they could develop into problems. That system saved the contractors $2.3 million in potential change orders.
Analyzing the Earth’s surface using LiDAR sends 32 times more pulses (and data) back to the sensors than conventional tomographic systems. In the comparison of efficiencies between using a topographic map produced by a human surveyor and a LIDAR map, humans win hands down in reflexes and parallel processing, but that was a 1970s comparison.
Undoubtedly, health care professionals, using LiDAR for in vivo studies, could benefit from the kinds of efficiencies and accuracies that these two industries get from using LiDAR.
Done right, and by design, a 10% increase in the accuracy of surface feature detection means that LiDAR applied in the study of human anatomy could yield a 10% increase in precision of detecting surface human features with in vivo studies being done.
Using LiDAR to achieve that level of accuracy is ridiculous.
Accuracy Considerations for Practical Applications
Real-World Factors Affecting LiDAR Measurement Quality
Most applications need the LiDAR data to be accurate, yet several factors can affect just how precise the measurements are. It’s important to know these factors to assess the quality of the data and pick the right systems to meet specific needs.
The system-related aspects lay down the basic accuracy potential of any LiDAR system. Timing accuracy hinges on the LiDAR system’s ability to measure time intervals accurately, with better timing found in higher-end systems in the order of picoseconds. However, a system that excels at timing and pulse width (which affects pulse resolution) is not enough if, in the end, the signal processing algorithms can’t handle weak, close-to-the-threshold return signals accurately. Better signal processing for weak return signals can improve range accuracy significantly, especially in situations where the range of objects has a more vertical profile than usual.
The precision of measurements is also affected by the size of the laser beam and how far the beam travels from the sensor. As the laser beam travels from the sensor, it naturally spreads out, creating a larger footprint at greater distances. This divergence means that at longer ranges, each laser pulse is hitting a larger area, potentially making averaged measurements across that area instead of providing a precise point measurement. For applications that require a lot of detail at long ranges, systems with minimal beam divergence are the way to go.
LiDAR measurement accuracy can be influenced by several factors, of which environmental ones are probably the most important.
1. Atmospheric conditions: Rain, snow, fog, and high humidity can scatter and attenuate the laser beam, reducing the effective range and introducing noise.
2. System component thermal changes: Temperature variations can cause system components to thermally expand or contract and can affect air density, all of which can introduce systematic errors.
3. Atmospheric turbulence: This creates variations in air density that can refract the laser beam, particularly over long distances or above heated surfaces.
Mobile LiDAR systems can face significant difficulties stemming from errors in positioning. These stem primarily from GNSS, and can be attributed to several culprit conditions: poor satellite geometry, signal obstruction in environments like urban canyons or under tree canopies, and even so-called multipath effects from signals that reflect off buildings and other surfaces before reaching a receiver. GNSS is also sensitive to weather effects. And when GNSS is not working well, the IMU has no way of knowing it either, and the two systems work together to produce an outcome that is sometimes less than ideal for the first.
Positioning performance is not just a concern for the moment at which a point is collected, but also for the time at which a line is collected.
Future Developments and Market Trends
The Evolving Landscape of LiDAR Technology
LiDAR technology keeps evolving quickly. Today, a number of key trends are influencing its future development and use. Understanding these trends is important for making strategic decisions about when to invest in which technologies and when to implement them.
Progress in hardware is working wonders for improving LiDAR. The reliance on moving parts in LiDAR systems has resulted in huge, expensive devices that are prone to breakdowns. New solid-state systems not only have proven reliability but also are small enough to be embedded in vehicles and other applications.
Opsys Technologies is one example of a solid-state vendor that has next-generation capabilities all but sewn up. The company recently unveiled a new system that offers high resolution and numerous other advantages over the most common operational forms of LiDAR. Multi-spectral capabilities are a really big deal because they allow the new systems to see in ways the old ones simply couldn’t.
Software breakthroughs are equally revolutionary and often stretch the potential of current hardware far beyond its limits. The computing power behind AI and its many variants—like machine learning, deep learning, and neural networks—allows these programs to do what they do best: automate the extraction and classification of features. Real-time analysis capabilities are advancing apace, with on-device processing delivering immediate results for time-sensitive applications like autonomous vehicle navigation and emergency response. Digital twin integration allows for the nearly instantaneous updating of digital models with new scan data, giving us ever-evolving living representations of physical assets.
The international market for LiDAR technology depicts the advances made in this field, with a forecast to reach a value of $12.81 billion in 2033, up from $2.31 billion in 2024, reflecting a compound annual growth rate (CAGR) of 19.3%. This growth is not confined to a single sector but is accompanied by a wide array of applications. The most robust growth is visible in the automotive field, where a CAGR of 24.5% is predicted to hold through 2033. Infrastructure is set to see a CAGR of 18.7% through 2033, while smart city applications draw a projected 22.1% CAGR over the same period. And then there’s LiDAR in an incredibly diverse field of application: forestry and agriculture, set to see growth at a 16.3% CAGR through 2033.
iScano Services Overview
Comprehensive LiDAR Solutions for Diverse Applications
iScano delivers complete solutions that hit head-on the major problems organizations encounter when they’re trying to implement LiDAR technology. With profound knowledge of many industries and applications, iScano guides its clients through the might maze of decision points associated with adopting LiDAR. From initial decision-making to ensuring that clients get the most out of their spatial data, iScano serves as a helpful partner.
Helping clients select the proper equipment is a fundamental aspect of iScano’s consulting operations. When it comes to choosing the right LiDAR system for a given task, the iScano personnel provide evenhanded, objective counsel. They aren’t trying to push a particular system or technology, so their advice tends to be reliably good. Their consultants consider necessary accuracy, required range, environmental conditions, and budget before coming up with a recommendation.
iScano employs experienced professionals to run the state-of-the-art equipment used to gather data for its services. That equipment includes mobile, airborne, and terrestrial LiDAR systems that can be deployed for any number of projects, whether large or small. These system types allow iScano to deploy the best data collection method for each project’s unique set of specifications, ensuring that the quality of the data collected far exceeds any project budget.
Point cloud processing stands as another foundational strength in the service portfolio of iScano. The specialists of the company possess many years of concentrated experience in the processing and in the analysis of point cloud data. Even though point clouds possess many similarities with other types of 3D data, they also have some unique features that make them more challenging to work with. Special tools and techniques are required to work with point clouds. The specialists of iScano use these tools and techniques to convert raw point cloud data into usable products.
The following client success stories demonstrate the real and substantial advantages of working with iScano. An engineering firm asked iScano to help with a highly complicated infrastructure project. No problem: The iScano team knocked that one out of the park. The iScano approach cut field time down by 45% compared to traditional methods of surveying. And the iScano solution saved 30% on overall project costs. So much for the iScano way cutting corners: They rather improved the overall quality of delivered data. The team registered an impressive 95% reduction in data gaps compared to other, older methods.
Conclusion
LiDAR technology is one of the most important developments in remote sensing and spatial data collection seen in recent decades. The fundamental principle of the technology—light detection and ranging, or laser altimetry, to create high-precision, 3D, as well as 2D, measurements—has allowed transformative applications in many different fields, from those related to driving and vehicle navigation systems to solving the mysteries of our ancient past.
LiDAR systems’ core components—laser emitters, optical sensors, scanning mechanisms, and processing units—work in concert to generate point clouds that capture the physical world with a level of detail and accuracy that’s hard to beat. Their data collection methods allow for all sorts of different applications and environments, making them a pretty versatile technology.
Although LiDAR measurement has many accuracy factors, current technological enhancements improve performance and decrease size, cost, and complexity to the point where LiDAR is now an accessible and versatile measurement tool.
LiDAR systems are an evolving technology, which we can expect to further integrate with other technologies; uncover new application areas; and, through these, achieve wider adoption. The future holds not only technical enhancements for LiDAR but also a proliferation of innovative uses for the technology that will help us to better understand and manage our physical environment.
Whether you’re a pro thinking about some very tailored applications of LiDAR technology or just a part-time amateur in its world, this guide gives you the basics and lets you know what to expect from the relatively few LiDAR systems on the market today, and what not to expect. Unlike some of the more fancy-pants guides put out by LiDAR manufacturers themselves, this one is low on hype and high on reality with LiDAR.
