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LiDAR captures distances from any object using Laser light

Understanding the Basics of 3D LiDAR Technology

3D LiDAR technology and processing software are increasingly becoming critical in numerous fields from people flow monitoring in Airports to Vehicles.


Light Detection and Ranging, also known as LiDAR, is a technology for remote sensing that measures distances in an environment.

It is accomplished by illuminating the environment with invisible light to the human eye and timing how long it takes for the light to reflect.

This advanced sensing technology enables robots and computers to accurately “see” their surroundings in three dimensions.

Unlike existing 2D-based perception technologies such as cameras, the 3D data from LiDAR produces highly detailed, accurate spatial measurements and works in various environments and contexts, such as during the night and under direct sunlight.

One of the non-technical advantages of deploying LiDAR at scale is its ability to operate without capturing any personally identifiable information, ensuring privacy by design.

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Shadowless perception with lidar

LiDAR technology does not capture any personal information.

And why should we talk about it?

LiDAR technology is increasingly critical across multiple fields, such as autonomous vehicle design, people flow monitoring in smart infrastructure, robotics, industrial safety, and environmental monitoring.

Are you curious about all the LiDAR application possibilities?

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If you want to get started working with LiDAR or even have a passing knowledge of it, read on. This article will teach you the essential information that you need to know about LiDAR.

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How Does a LiDAR Work?

LiDAR technology was developed as a way to measure distance accurately using light.

In LiDAR, a laser pulse is sent from a source (transmitter) and reflected from objects in the scene.

The system receiver detects the reflected pulses, and since the speed of light is constant, the time of flight (ToF) is used to develop a distance map of the objects in the scene (i.e., the amount of time that elapses reveals how far away the object is from the source of the light).

Laser pulses

In the figure above, we illustrate how one laser pulse measures the distance to the object.

In practice, a LiDAR sensor can send millions of laser points per second in different directions to obtain a comprehensive understanding of its surroundings.

The instantaneous three-dimensional data acquired by the sensor is referred to as a point cloud and is meant to be represented by the term “Frame” in 3D LiDAR. This is analogous to a picture taken by a camera. LiDAR systems will typically provide data in some 20 frames per second.

With the appropriate software, you can combine many frames (or point-clouds) to create a 3D Map, that many people call a “Point-cloud 3D Map”:

Understanding the basics of 3d lidar technology

Point-cloud 3D map generated over an airport scene

The source of measurement can be a ground-based unit or something flying overhead, such as vehicles, satellites, airplanes, or robots:

Understanding the basics of 3d lidar technology - backpack

An example of a 3D LiDAR mapping kit in a backpack

LiDAR can measure the distance to almost anything, including terrain, vegetation, buildings, and other moving objects like vehicles and people.

Where Does LiDAR Come From?

LiDAR is an old technology, but it’s been rebranded and redeveloped for new applications. The basic idea behind LiDAR is to measure distance with light.

The word “LiDAR”, Light Detection and Ranging, dates back to the 1950s when it was used as a short-range radar system to measure the distance to buildings, trees, and other obstacles.

It was a precursor to the modern-day scanning technique called LiDAR (pronounced LIE-dar). In the early 1970s, scientists began experimenting with LiDAR to measure the distance to the earth’s surface. In 1971, Apollo 15 astronauts used LiDAR to map the moon’s surface. In the 1990s and 2000s, LiDAR sensors started to map terrain and create digital elevation models.

LiDAR has been a valuable tool in construction, forestry management, and mineral exploration for decades.

Forestry mapping with lidar by outsight

Forestry mapping with LiDAR, processed by Outsight’s software

In 2021, the program LiDAR HD started collecting high-density LiDAR data over the entire metropolitan area and overseas French territories to meet the needs of observation and spatial analysis in many areas of public action (risk prevention, observation of forest resources, land use planning, etc.).

In the early 2020s, Apple started including LiDAR technology in its iPads to enhance the environment’s 3D modeling for augmented reality applications.

What’s the difference between a LiDAR and a camera?

LiDAR and Cameras serve different purposes in detecting objects, focusing on other aspects.

  • A Camera captures colors, but it has no notion of distance (a big object far away is the same size as a small object close to the camera)
  • A LiDAR captures Spatial Data, which is objects’ distance, shape, volume, and velocity.

Consequently, if one wants to answer a question like “Is it a cat or a dog?” a camera is a more appropriate tool, thanks to the precise colored image and the right computer vision software.

However, if one wants to answer more sophisticated questions like “What’s the size of this object?” the 3D LiDAR is a more appropriate solution.

The table below summarises which object features are detectable by each technology.

Table lidar vs camera

Another key difference between LiDAR and cameras is that the latter are passive sensors, meaning they depend on the external light to grab the colors (no light → no information).

In contrast, LiDAR is an active sensor that generates light to grab spatial information. Thus, it works in any lighting condition, including complete darkness (no light → still has information).

As a result, LiDAR allows vehicles and robots to identify other nearby vehicles, people, and other objects in the environment in almost any situation:

Tracking pedestrians robot

A detailed comparison of LiDAR, Radar and Camera Technology

This article explores the capabilities and limitations of each type of sensor, to provide a clear understanding of why LiDAR has emerged as a strong contender in computer vision tech race.

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Learn more about the differences between these sensors (LiDAR vs Camera, LiDAR vs Radar)

Dive deeper

You’re now ready to get into more details, thanks to the continuation of this article:

Understanding How Lidar Works

3D LiDAR is a complex technology that enables unprecedented Spatial Intelligence. Many engineering choices are possible when building a new device.

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Conclusion

LiDAR is a remote sensing technology that measures the distance to a target by illuminating it with laser light and measuring the reflected pulses with a detector.

LiDAR has been used to measure and map the earth’s surface since the early 1970s, and today it has been adapted in order to fulfill the needs of many applications in different markets.

However, it still offers integration challenges, such as a lack of standardization among manufacturers, high computer processing demand, and overall complexity.

Not All LiDAR Sensors Are Equal: Key Differences Explained

This article explores LiDAR differences and why customers use multiple vendors to meet their needs.

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Today, it’s finally easy to overcome these challenges thanks to Outsight’s 3D Software Solutions.

Ready to unlock the power of LiDAR perception?

Contact a Product Specialist if you want to know more!



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Frequently Asked Questions

  • What frame rate does a 3D LiDAR sensor typically output?

    Most commercial 3D LiDAR sensors output point cloud data at around 20 frames per second, broadly comparable to a standard video feed. Each frame contains millions of individual distance measurements captured across a wide field of view. At that rate, a perception pipeline can track the position and velocity of moving people and vehicles with enough temporal resolution to detect sudden stops, direction changes, or collision-risk trajectories in real time. Outsight's SHIFT platform processes this raw point cloud stream through a sub-50ms end-to-end pipeline, meaning the gap between sensor capture and actionable output remains well within a single frame interval, a property that matters in dense environments like airports or factory floors where conditions change rapidly.

  • What are the main integration challenges when deploying LiDAR at scale across a large site?

    Three challenges recur in large-scale LiDAR deployments. First, sensor manufacturers use incompatible data formats and communication protocols, so software that natively supports only one vendor creates lock-in and limits flexibility when mixing hardware across a site. Outsight addresses this through the SHIFT platform, which is LiDAR-native and maintains multi-vendor compatibility across Hesai, RoboSense, Ouster, Velodyne, and Seyond hardware. Second, processing millions of laser points per second per sensor demands significant edge compute, especially when dozens of sensors feed a shared 3D model simultaneously; Outsight's pipeline handles this within a sub-50ms end-to-end latency. Third, calibrating sensor positions precisely enough that their point clouds stitch into a single, spatially consistent scene requires specialized tooling and site-survey expertise, a complexity evident in large deployments such as Dallas Fort Worth, currently the world's largest 3D LiDAR airport deployment.

  • Can LiDAR detect the velocity of a moving object, or just its position?

    LiDAR captures both position and velocity. Because each frame records the precise 3D position of every detected surface point, comparing consecutive frames lets the processing software compute displacement over time and derive velocity vectors per tracked entity. This requires no Doppler hardware: the spatial precision of the point cloud, combined with the sensor's frame rate, is sufficient to measure walking speed, running speed, and vehicle speed accurately enough for safety alerting and flow analytics applications. Outsight's SHIFT platform applies this principle in real time, with a sub-50ms end-to-end pipeline, enabling deployments such as Dallas Fort Worth Airport and BMW factories to act on velocity data the moment it is generated.

  • Is the LiDAR on an iPad the same technology used in airport passenger tracking?

    The underlying physics are identical: both emit laser pulses and measure the time of flight to construct a 3D depth map. The engineering priorities diverge sharply, however. Consumer LiDAR on mobile devices is optimized for short-range augmented reality, typically covering a meter or two at low point density. Infrastructure-grade LiDAR deployed in airports operates at ranges of tens of meters across wide fields of view, emitting far higher point densities per second and designed for continuous 24/7 operation in demanding lighting and environmental conditions. Outsight builds on this infrastructure-grade category through its SHIFT platform, processing multi-vendor LiDAR streams in real time to track the movement of people and vehicles anonymously across large airport terminals, with deployments at sites including Dallas Fort Worth and Paris-Charles de Gaulle.

  • Why does LiDAR need software to be useful, and what does the software actually do?

    Raw LiDAR output is an unstructured stream of 3D coordinates: it records where laser pulses reflected, not what those surfaces belong to. Software performs the interpretation layer. It segments the point cloud into discrete objects, classifies each object by shape and motion signature (pedestrian, vehicle, robot), assigns a persistent anonymous ID, and tracks that entity frame to frame across the scene. Without this processing stage, a point cloud is a dense but unreadable map of dots. Outsight's SHIFT platform illustrates what mature LiDAR software delivers at scale: a sub-50ms end-to-end pipeline that converts raw point clouds into a real-time, anonymous 3D representation of how every person, vehicle, and robot moves through a physical site, turning spatial measurement into actionable operational intelligence.

  • How does LiDAR handle moving objects that partially overlap or walk close together in a crowd?

    Proximity and partial overlap are handled at the segmentation stage, where software uses shape priors (the expected 3D bounding volume of a standing adult) and motion continuity to separate entities that share a cluster of points. When two people walk side by side and their point clouds merge momentarily, the tracker uses predicted trajectory from prior frames to maintain distinct IDs through the occlusion event. Fusing multiple sensors into a shared point cloud from different angles (shadowless perception) reduces how often merges occur in the first place, because at least one sensor typically has a clean sightline to each individual. Outsight's infrastructure-based approach applies exactly this multi-sensor fusion logic through the SHIFT platform, processing the resulting detections in a sub-50ms end-to-end pipeline across dense crowd environments such as airports and train stations.