by Chris Woodford.
Last updated: April 23, 2020.
Don't know where you're going... don't know how to get there? Then you'll need a map. But what if there is no map? Hmmm.... okay, then
what if part of your brain could rush ahead of you, quickly sketch a
map, and feed it back to the rest of your brain to help you find your
way? It sounds completely bonkers, but it's exactly how self-driving
cars work—using a neat 3D map-making technology called LIDAR (which
stands for LIght Detection And Ranging). As the name suggests, LIDAR
works a little bit like radar (radio-wave navigation used by ships
and planes) and sonar (underwater detection using sound, mainly used
by submarines), though it has quite different applications, from
factory-floor robots and driverless taxis to coastal mapping and
measuring deforestation. How exactly does it work? Let's take a
Artwork: The basic idea of navigational LIDAR: the self-driving car (blue) "sees" by bouncing a spinning laser beam (orange lines) off obstacles and detecting its reflections (green lines). The time it takes for the beam to return tells us how far each obstacle is from the car. In this way, LIDAR creates a 3D map of the dynamic environment around the car much more quickly than the car itself is driving.
Look around you. What you see is a 3D color map of your immediate
environment that your brain has built (mostly in real-time) using the
light rays soaked up by your eyes. If you were a
robot with a couple
of digital cameras stuck on your head, you could build yourself a map
of a room in much the same way, but it wouldn't be anything like as
informative and useful. You wouldn't necessarily know that one object
was nearer than another or that a growing black blob in the middle of
the room was a cat creeping toward you. As a human, you know these
things because your brain processes visual information using a
lifetime of experience of what a growing black blob actually means.
But robots don't have the same encyclopedic life experience to draw
on, which means they're at a natural disadvantage when it comes to
"seeing" the world.
That's why autonomous robots (ones that control themselves) and
self-driving cars often prefer to look at the world a different way,
using LIDAR systems instead of cameras. Where a camera-based eye
snaps an instant 2D photo of a scene that has to be processed and
interpreted to find out what it's looking at, LIDAR makes millions of
measurements of depth information in all directions
simultaneously—and it's often quicker and easier to turn that data
into a map you can use for navigation, in real-time.
Photo: Look no eyes! Sandstorm, a self-driving car from the 2005
DARPA Grand Challenge, uses sensors of different
kinds to "see" where it's going, including short- and long-range
LIDAR. The long-range LIDAR scanner is the round, white object on top of the roof;
it can "see" a distance of 150m (450ft); the short-range LIDAR units are mounted on the front of the car near the radiator
and to the left of the windshield area. Photo courtesy of Dan Homerick published on Flickr
under a Creative Commons Licence.
What is LIDAR?
Take a look at a factory-floor robot or a self-driving car. What's
that weird, whirling can thing sitting on top like a helmet? That's
the LIDAR: it's spinning round, firing invisible
laser beams in all
directions, catching the reflections, and measuring how long the
beams take to return so it can figure out what obstacles are nearby
and how far away they are. So the basic concept of LIDAR is exactly
the same as radar and sonar. With radar, you might have a
firing out a beam of coded radio waves and listening for a return
beam reflected off some nearby object (another plane about to crash
into you); it uses the time taken for the beam to return to figure
out how far away the object is. With sonar, you do the same thing
underwater, only using sound waves (because ordinary light and radio
waves don't travel through water very far). In everyday, on-land
situations—driving down the street or navigating through a
building—reflected laser light turns out to be a better source of
information than either radio waves or sound, and that's why LIDAR
has become so popular: it's simple, reliable, and relatively
low-cost, if still very expensive for amateur or hobbyist use
(currently, we're talking thousands of dollars).
So you can use LIDAR data to build a real-time map of the streets
through which a self-driving car is trying to navigate or the factory
a robot has to trundle through, but you can also it in other ways.
Long before self-driving cars became such a hot topic, geographers
and atmospheric scientists were using LIDAR in a more "passive"
way, to draw detailed aerial maps of Earth or the atmosphere.
(Climate scientists proposed studying the weather with laser-radar as far back
as the mid-1960s.) For these sorts of applications, instead of using
a spinning LIDAR, you mount a LIDAR unit underneath a plane or a
helicopter and have it scanning across the ground as the plane flies
across a precise trajectory. While visible light isn't much use for
scanning underwater, it is possible to use blue-green laser light to
make LIDAR scans of the seabed, for example.
Artwork: LIDAR is now probably best known for its use in robots and self-driving cars, but it has many other applications.
High spectral resolution LIDAR (HSRL), often operated from scanning airplanes like this, is used to study Earth's atmosphere and oceans. It works by sending out LIDAR signals, then examining the spectrum of the radiation that's "backscattered" (which means roughly rather than exactly reflected) by molecules and aerosols in the atmosphere.
What kind of data do you get from it?
LIDAR data can be used by itself or combined with data gathered in
other ways. In the case of an aerial map, LIDAR systems typically
also use GPS (satellite navigation); in self-driving cars, LIDAR
tends to be used alongside GPS, onboard sensors (like
speedometers), inertial guidance systems and gyrocompasses, and
navigational data from stored maps (think Google Street View). What you end up with is
called a "point cloud": a three-dimensional array of LIDAR
measurements related to specific GPS coordinates. For a moving
sensor, like the one on a self-driving car, you end up with millions
of data points stretching as far as 60m (200ft) away from you in all
directions, accurate to just a few centimeters.
Photo: Mapping the Moon. NASA engineers and astronauts test K10 lunar rover scanning units at Moses Lake dunes. The rovers have ground-penetrating radar and LIDAR scanners that can make 3D maps of the Moon's terrain, both above and below ground. Photo courtesy of NASA on the Commons.
What does a LIDAR system consist of?
To make a LIDAR map, you need a
laser and something that picks up
its reflected light; something to move your laser beam and make it
scan all around you; and typically also a GPS receiver so you can
figure out where you are and which bit of the world your LIDAR data
Photo: What do you need for LIDAR scanning? Something like this truck, set up by
researchers at the US Department of Agriculture to fire green LIDAR beams into the air from a scanner on the roof. LIDAR is widely used for mapping the world and studying its atmosphere. USDA researchers have been using it for 20 years to study how agriculture and farming affect the atmosphere—everything from water uptake by growing trees to the air pollution made by intensive animal farming. Photo by Peggy Greb courtesy of USDA Agricultural Research Service.
Will any old laser do? One of those big, buzzing lasers like
Goldfinger wanted to use to cut James Bond in half? No! Typically,
we're talking about a
semiconductor diode laser, more like the ones
you'd find in a
laser printer or
CD player only more powerful.
Instead of firing out visible light (which has wavelengths of around
400–700 nanometers), a self-driving car would use a LIDAR with an
invisible, near-infrared laser (around 900–1100 nanometers).
Underwater LIDAR scanners use green laser light with shorter
wavelengths (around 530 nanometers) in the middle of the visible
range. There's obviously some danger to people's eyes when you start
firing infrared laser beams all over the place—and more danger with
cars hurtling down the street than with airplanes scanning distant
rainforests from the sky. Generally speaking, the further a LIDAR
laser needs to penetrate safely, the higher the wavelength it will
use—because light of longer wavelength has a shorter frequency and
lower energy. The latest self-driving car lasers are using laser
wavelengths of 1550 nanometers to scan up to 200 meters ahead,
compared to just 30–40 meters for a high-powered laser working at
The photodetector in a LIDAR system is a kind of
photoelectric cell made of silicon or gallium arsenide that's designed with maximum
sensitivity for whatever light wavelength the laser is using.
Different types of detectors are used according to the kind of range
over which the LIDAR system is operating. Short-range LIDAR systems
typically use simple silicon photodiodes. Long-range systems use what
are called avalanche photodiodes (APDs).
These work a bit like Geiger counter radiation detectors, turning a single
incoming photon of light into a measurable avalanche of electrons
(an electric current that can be measured), so much lower light levels can be detected.
Many APDs can be built into a single chip to create a kind of checkerboard of detectors called a
multi-pixel photon counter (MPPC).
Spinning a laser at high speed sounds like a bit of an engineering
nightmare—all those tangled wires and vibrating metal cases—but,
fortunately, we can get by without doing that. All we have to do in a
LIDAR system is scan the beam, not the laser itself, and for
that we can just use a rapidly rotating
mirror. Modern LIDAR systems
use microscopic moving mirrors based on MEMS (MicroElectroMechanical Systems) technology, mounted on microchips and
similar to the ones you find in digital projectors; other use bigger
mirrors roughly the size of coins.
Animation: 1) In theory, LIDAR lasers are scanned by firing them off a fixed mirror (top) and a rapidly rotating one (bottom).
Artwork: 2) In practice, it's not quite so clunky, and modern LIDARs tend to use very small microscopic mirrors based on MEMS technology. Each tiny mirror segment (pink) tilts on a hinge (green), attracted by the electrically charged plates below (blue). Artwork from US Patent 4,710,732: Spatial light modulator and method by Larry Hornbeck, Texas Instruments, 1987, courtesy of US Patent and Trademark Office.
What's LIDAR used for?
Though robots and self-driving cars are the kinds of LIDAR
applications you're likely to read about in the technical press, the
most common applications to date are in geographical and atmospheric
mapping. People like the USGS (US Geological Survey), NOAA (National
Oceanographic and Atmospheric Administration), and NASA have been
using LIDAR for make maps of Earth and space for decades. Climate
scientists use it to probe the composition of the atmosphere and
study things like clouds, aerosols, and
oceanographers use it to track coastal erosion; and botanists are
flying up in airplanes right now using LIDAR to measure the
ever-changing patterns of Earth's forests.
Photo: SeaHunter, a self-guided prototype aerial vehicle, uses
LIDAR to navigate. Photo by Grant P. Ammon courtesy of
We can also use LIDAR to study the gas composition of the
atmosphere. Different gases absorb different wavelengths of light by
different amounts, so we can study the gases in a particular location
remotely by firing two different wavelength laser beams into it from
a plane or a helicopter and comparing how much of each wavelength is
absorbed or reflected. This system, which is called Differential
Absorption LIDAR (DIAL), can be used for everything from detecting
leakages from gas pipelines to measuring
One of the most common uses of LIDAR is in police speed guns.
Though we typically think of them as radar guns (and static highway
speed cameras, such as Gatsos, do use radar), handheld guns are much more likely to
use 905-nanometer LIDAR lasers, which are cheap, safe, and very
A brief history of LIDAR
- 1930s: Three decades before the invention of lasers, scientists experiment with measuring the composition of the
atmosphere using sweeping searchlight beams.
- 1958: Charles Townes and Arthur Schawlow invent the maser (the
original, microwave laser); their student Gordon Gould also makes
- 1960: Theodore Maiman builds and demonstrates the first
- 1962: MIT scientists measure the distance between Earth and the
Moon using a reflected laser beam.
- 1965: Stanford Research Institute's Ronald Collins files a
patent for a
laser-radar LIDAR system that can be used to study Earth's atmosphere and weather.
- 1969: Daniel Hickman and John Hogg publish an
influential scientific paper describing
how airborne lasers can be used for making measurements of ocean depth.
- 1971: Apollo 15 astronauts use LIDAR to map the surface of the
- 1974: Alan Carswell of York University, Toronto invents a laser
range-finder and founds a company called Optech to sell it. Over the
next few years, Optech perfects the idea of scanning a laser remotely
to make maps.
- 1976: The first textbook about LIDAR is published.
- 1985: Optech begins selling a product called the Larsen-500, one
of the first commercial LIDAR systems.
- 1990s: LIDAR is widely used for geographical mapping.
- 1994: NASA takes LIDAR into space on the Space Shuttle Discovery.
LITE (Lidar In-Space Technology Experiment) is the first time LIDAR
had been used to study the atmosphere from space.
- 2005: LIDAR systems make the headlines as the eyes behind
self-driving cars in the US military's DARPA Grand Challenge.
- 2008: NASA's Phoenix Lander takes an Optech LIDAR scanner to
Mars to study the planet's atmosphere.
- 2015: DARPA announces that it has created Sweeper, a miniature
LIDAR system on a single chip ("Sweeper" stands for Short-range
Wide-field-of-view Extremely agile Electronically steered Photonic
- 2017: The Environment Agency of England and Wales
announces it will LIDAR scan the whole of England and make the data publicly available.
- 2020: Apple includes LIDAR in its latest iPad to improve 3D modeling of the environment around it
for augmented reality applications.