What is LiDAR?

Essentially, LiDAR is a ranging device, which measures the distance to a target. The distance is measured by sending a short laser pulse and recording the time lapse between outgoing light pulse and the detection of the reflected (back-scattered) light pulse. 

Operational LiDAR systems face several well-known challenges, which can vary depending on the specific type of system. Some common examples include:

• Signal Isolation and Rejection: The emitted probing beam is usually much stronger than the return beam. It’s important to prevent the probing beam from reflecting or scattering back into the receiver, as this can saturate the detector and prevent it from identifying external targets.

• Spurious Returns from Atmospheric Debris: Particles or debris in the atmosphere between the transmitter and the intended targets can produce strong, unwanted signals. These spurious returns may interfere with the reliable detection of the actual target.

• Optical Power Limitations: Higher beam power improves accuracy but increases operational costs. Balancing power and cost is a key consideration for LiDAR system designers.

• Scanning Speed and Safety: Rapid scanning can raise safety concerns if the laser operates at frequencies harmful to human eyes. Solutions like flash LiDAR, which illuminates a large area at once, and using eye-safe wavelengths, help address these safety issues.

• Device Crosstalk: Signals from nearby LiDAR devices can interfere with each other, making it difficult to distinguish between different sources. Techniques such as signal chirping and improved isolation are being developed to overcome this challenge.

• Cost and Maintenance: LiDAR systems are generally more expensive than some other sensor technologies. However, ongoing development aims to reduce costs and make LiDAR more accessible for wider use.

• Rejection of Returns from Unintended Objects: Unwanted signals can also occur in clear air, not just in the presence of atmospheric debris. Addressing this often involves minimizing the beam size at different target distances and optimizing the receiver’s field-of-view to better filter out irrelevant signals.

These challenges are the focus of active research and development to improve the reliability, safety, and affordability of LiDAR technology.

The application areas for LiDAR are deep and varied.  In atmospheric sciences, LiDAR has been used for the detection of many types of atmospheric constituents. It has been used to characterize aerosols in the atmosphere, investigate upper atmospheric winds, profile clouds, aid the collection of weather data, and many other applications. In astronomy, LiDAR has been used to measure distances, both for distant objects such as the moon and for very near objects. In fact, LiDAR is a crucial device for improving the measurement of the distance to the moon up to millimeter precision. LIDAR has also been used to create guide stars for astronomy applications. 

LiDAR data is often collected by air, such as with this NOAA survey aircraft (right) over Bixby Bridge in Big Sur, Calif. Here, LiDAR data revelas a top-down (top left) and profile view of Bixby Bridge. NOAA scientists use LiDAR-generated products to examine both natural and manmade environments. LiDAR data supports activities such as inundation and storm surge modeling, hydrodynamic modeling, shoreline mapping, emergency response, hydropgraphic surveying, and coast vulnerability analysis.

Source: NOAA - https://geodesy.noaa.gov/INFO/facts/lidar.shtml

One fascinating application for LiDAR is situational awareness for things like autonomous navigation. The situational awareness system for any moving vehicle needs to be aware of both stationary and moving objects around it. For example, radar has been used for a long time in detecting aircraft. LiDAR has been found very helpful for terrestrial vehicles because it can ascertain the distance to objects and is very precise in terms of directionality. The probing beams can be directed to precise angles and scanned quickly to create the point cloud for the three-dimensional model. The ability to scan quickly is key for this application since the situation surrounding the vehicle is highly dynamic.