What LiDAR range really means: A practical breakdown

After price, range is the most important question you should ask when looking for a LiDAR solution. The problem is “range” is one of the most abused and misunderstood specs in the industry. Lab numbers and real-world performance live in different universes, and during procurement those universes inevitably collide.

Here’s a handy, practical framework we’d recommend to cut through the marketing fog. There are four range types, clear definitions, and a fast mental math trick to set realistic expectations.

The four ranges

• Sensor Range

  • What it is: The lab-derived maximum distance at which the sensor can detect a single return under ideal conditions. Tests are often long in duration and best-case scenarios are optimized to show the highest number.

  • Practical note: For field use, you’ll almost never get this number. This is the datasheet spec.

    
    

• Detection Range

  • What it is: The maximum distance at which a perception system can detect that “something” is moving (rough indication, not tracked or identified). Assume at least human size.

  • Practical note: This is the first operational threshold you’ll care about for alarms or presence detection.

    
    

• Tracking Range

  • What it is: The distance at which a perception system can reliably track a human-sized object (about 90% accuracy) — it knows position and motion, may have a rough idea what the object is but isn’t certain.

  • Practical note: This is the range you need for reliable motion prediction and safety use-cases.

    
    

• Classification Range

  • What it is: The distance at which a system can track and correctly classify a human-sized object with ~90% accuracy — i.e., it knows what it is and where it’s going.

  • Practical note: This is the range required to distinguish humans from animals, vehicles, or other objects with low nuisance-alarm rates.

Quick rule-of-thumb math: start with the Sensor Range on the datasheet and divide by two for each step:

• Sensor Range = datasheet number (e.g., 200 m)

• Detection Range ≈ Sensor / 2 (100 m)

• Tracking Range ≈ Sensor / 4 (50 m)

• Classification Range ≈ Sensor / 8 (25 m)

These estimates align well with empirical results and give you conservative, operationally useful numbers rather than marketing hype.

Why these conservative numbers matter

Point clouds at longer distances are sparse. For example, a 128-beam LiDAR yields only ~25–35 points on a human at ~25 m - borderline for reliable classification, and easily confusable with a deer or other object. In security, that confusion is the difference between a true alarm and a nuisance alarm - and nuisance alarms cost time and money.

A real-world failure mode: One vendor was looking for a vehicle-safety solution requiring high-accuracy with a detection range of 50m. One sensor vendor claimed a 100m range, but in testing the unit lost track of dark vehicles past ~25m. No perception tuning fixed it. Had they applied the divide-by-two rule to the vendor’s 10m Sensor Range, they would’ve expected a Tracking Range of around 25m - exactly what we observed. When lives are on the line, reliance on datasheet Sensor Range without practical translation is unacceptable.

Multi-LiDAR setups These figures assume a single LiDAR unit. Overlapping, well-calibrated multi-sensor arrangements can extend effective range by roughly 50% in ideal setups (e.g., a 50m Tracking Range could approach 75m).

The bottom line is that Datasheet Sensor Range is a lab number. For operational planning, use Detection, Tracking, and Classification ranges (apply the divide-by-two rule) to set honest expectations. Doing so avoids surprises, lowers operational risk, and helps choose the right sensor for the job.

Contact us to learn more about our LiDAR ranges and solutions.