Time-of-flight (ToF) sensors have become increasingly popular as machine learning and computer vision systems develop. While ToF sensors have grown in prominence over the last decade, the fundamental principles that enable them to work have been around for much longer. Lightning-fast, powerful computational systems now allow ToF sensor technology to grow at a rapid pace.
Radar: The first ToF sensor
Radar ― a shortening of the term "radio detection and ranging" ― utilizes radio waves to find the distance between two objects.
1. One object contains a radio source and receiver that emits a pulsing radio wave.
2. The second object reflects that wave.
3. The first object receives the reflected radio signal.
The signal must be pulsing so the receiver can detect the reflected signal's phase difference. We establish this phase difference using the "flight" of the radar signal. In radar systems, the signal frequency ranges from 5 megahertz to 130 gigahertz—roughly the frequency of radio waves on the electromagnetic spectrum. Radar could be considered the first time-of-flight sensor.
How time-of-flight sensors work and their applications
The term "time-of-flight" is a catch-all for any technology that calculates the time a signal takes to move through space. Modern ToF sensors utilize light as the source and receiver signal ― rather than a radio wave ― to perform the same type of calculations as radar.
You can find ToF-based technology in a variety of applications, such as:
- Industrial safety
- Autonomous vehicles
- Object detection and categorization
- Virtual and augmented reality
Light-based ToF sensors like LiDAR have risen in popularity over the last decade because they can handle high-frequency signals like light and lasers. ToF sensors may use the same fundamental principles as radar, but ToF has several key advantages and disadvantages compared to radio-waves.
Time-of-flight distance measurement
Compared to radio-waves, time-of-flight sensors can measure smaller distances, but with much higher resolution. Like radar, a continuous wave source can modulate or pulse a light or laser to create a unique signal pattern for the receiver signal's comparison calculation. However, a light-based ToF emitter's higher frequency enables the signal to take on various wave patterns, including an oscillator's square waves.
Square wave signals are advantageous, especially in ToF operations. Digital hardware can interpret and process square waves better than the analog sinusoidal signals more commonly found in radar systems. The ability to utilize digital hardware as the primary sensing mechanism for ToF sensors makes it possible to develop large visualization systems using relatively inexpensive and small hardware.
3D ToF camera sensors & point cloud scanners
We currently see ToF technology at work in computer vision applications that develop three-dimensional images through numerous technological advances. Conventional cameras map only two-dimensional, color-oriented images made up of individual pixels plotted in a grid.
Highly accurate ToF sensors, however, add the third dimension to conventional photographs at nearly 1:1 pixel ratios. They also create point clouds that can visually represent a single pixel in the X, Y, and Z coordinates of the camera's field of view. ToF sensors can even add a third dimension to videos ― which are, in their most basic sense, a collection of images ― and create a dynamic three-dimensional point cloud and real-time depth-mapped video stream.
Best ToF sensor development tools
Here are some of our favorite tools for time-of-flight technology development:
- SparkFun ToF sensor
- Adafruit ToF sensor
- Analog Devices' more advanced 3D time-of-flight Sensor Development board
Just as radar revolutionized many aspects of scientific research and communication, advanced ToF technology, like 3D ToF camera sensors, will continue to expand our ability to understand our world. That is, if modern processors' computing power can keep up.
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