LiDAR vs. NRTK vs. Vision: The Ultimate Guide to Robot Mower Navigation Technologies (2026)

The navigation technology used in a robotic mower defines its performance.

The early robotic mowers used basic tech, which is why they couldn’t mow complex lawns and handle obstacles. And the latest models incorporate advanced tech, and that’s why they take user experience to the next level and put lawn mowing on automation.

This is a beginner’s guide to the latest robotic mower navigation technologies. We will talk about LiDAR, NRTK, and Vision navigation and compare all of them.

By the end of this guide, you’ll know what these technologies do and how, and you’ll be able to select a better option for future lawn care.

What is LiDAR Navigation in Robot Mowers?

LiDAR stands for Light Detection and Ranging. It’s a navigation method that relies on pulsed lasers.

A LiDAR sensor is mounted on the robotic lawnmower. It emits the laser beams and receives the bounced signals.

It throws a pulsed laser in a direction, and when it reverts back after hitting an object, the mower calculates the distance. Thus, it knows where the obstacle is.

Not only that, but it can also create a 3D map of the environment to help the mower understand everything around it. It can picture trees, flowerbeds, slides, and other obstacles. No matter how complex the lawn is, it works perfectly well.

What is NRTK Navigation in Robot Mowers?

NRTK stands for Network Real-time Kinematics. It’s the advanced version of RTK. Let’s understand RTK first to have a better understanding of NRTK.

RTK is satellite-based position technology. It is a high-precision technique to improve the standard GNSS (Global Navigation Satellite Systems). The result of the standard GPS is accurate to 1-5 meters, while RTK achieves centimeter-level accuracy.

RTK relies on only one reference station. Its coverage area is limited, and its accuracy degrades as the distance between the station and the robot mower increases to 15km. Secondly, its reliability is low as the station could fail due to a power issue, blockage, or loss of signals. And if that happens, it means the mower cannot rely on its RTK technology to navigate.

NRTK is the improved version, as it works through multiple reference stations. It uses the CORS (Continuous Operating Reference Stations) network to mitigate the distance errors and failures. If one reference point fails, there are others to provide the positioning data. Secondly, because of multiple reference points, it can work in different locations as the distance never increases by more than 15km.

Thus, NRTK provides the mower with an absolute global position. With the help of this, the mower knows the boundaries of the lawn without needing physical wires. It’s perfect for the mower’s safety as well as mowing multiple zones.

Keep in mind that this navigation system is not for detecting obstacles. It only provides the mower’s location. For obstacle detection, the mower has to opt for Vision or LiDAR technology.

What is Vision Navigation in Robot Mowers?

Vision navigation is relatively simple to understand. It employs one or more high-resolution cameras to capture lawn imagery.

Once the images are captured, they are processed with an advanced AI or Machine Learning algorithm to detect obstacles in the environment and create a complete picture or map. It can easily distinguish between different kinds of obstacles, such as trees, pets, flowerbeds, etc. It interprets every corner and edge.

With this technology, an accurate map of the lawn is created in real-time. As the mower moves forward, it makes informed decisions according to the map. It knows where to stop and what to dodge.

LiDAR vs. NRTK vs. Vision: What’s the Difference?

Core Mechanism

The primary difference between these technologies is the core mechanism.

LiDAR emits laser pulses using its sensor. There are thousands of pulses per second that travel in different directions. When these pulses hit any object, they bounce back to the sensor. The time of travel is used to determine the distance between the object and the mower. Thus, it helps the mower know lawn obstacles.

NRTK is an advanced form of GNSS or GPS that uses satellite signals to provide the absolute position of the mower. It works with multiple reference points to ensure reliable performance and reduce failure.

The Vision technology uses cameras to capture images of the lawn, and then it processes those images to retrieve relevant data, such as obstacles and their types. It’s based on AI algorithms and assists in obstacle detection.

Primary Output

LiDAR creates a 2D or 3D map of the lawn. It’s actually a point cloud map to identify the relative positions (distance) of the objects. The map is detailed and rich enough to provide data on every obstacle around.

The output of NRTK is X, Y, and Z coordinates. It shows the absolute global position. These coordinates refer to the latitude, longitude, and altitude to picture the position of the mower in the lawn.

It creates an image of the lawn for contextual understanding of objects. The mower understands its position and the direction and distance of objects.

Primary Strength

LiDAR is the best for mowing in complex lawns. It detects objects instantly as the laser beams travel extremely fast. It knows where the trees, pets, slides, toys, and other obstacles are to navigate through the area without harming or damaging anything. Even if the light is low, such as at night, it can work well.

NRTK precisely calculates the mower’s position. It eliminates the need for boundary wires. Users can only set the boundaries through GPS/GNSS positioning, and the mower stays within that area. It’s an ideal option for large lawns and mowing different zones.

Visual is the only technology that not only detects objects but also identifies them. It can classify objects to know whether it’s a flowerbed, pet, fence, grass edge, or anything like that. Which means it has a better understanding of your lawn.

Accuracy

All three navigation methods serve different purposes, so we have to evaluate the accuracy based on the application.

If we look at the global positional accuracy, there is nothing better than NRTK. It offers absolute coordinates that are accurate up to centimeters. LiDAR does not give absolute positioning. Instead, it offers a relative position from the starting point or charging station. Its accuracy reaches up to 2-5cm. Vision’s accuracy is the least in this regard, and it’s highly dependent on the landmarks.

When it comes to mapping and boundary accuracy, LiDAR is the best. It can detect local boundaries most accurately. The distance it measures is within millimeter-level precision. NRTK is also extremely accurate, but it follows a virtual boundary or digital map, which could be a bit off by a few centimeters. Finally, we have the Vision tech, which is good at detecting the boundaries of objects and makes an estimation based on the pixels, so it could be off by a few centimeters.

For obstacle detection, LiDAR is highly accurate. Its laser pulses can detect objects in real-time with exceptional accuracy. It knows exactly how far the object is. Vision also detects obstacles, but its distance accuracy is lower. It measures the distance based on the pixels; it isn’t that precise. And as far as the NRTK is concerned, it doesn’t detect obstacles.

Area Coverage

NRTK can offer large-scale coverage. Be it a golf course or a large commercial property, it can offer exceptional performance. There is almost no limit to this, as it depends on the satellite signals, so you can mow with it in large areas. It works on coordinates, which is why it precisely knows where it is. Mowing lawns up to 2-5 acres is not difficult with NRTK.

Vision navigation technology comes in second place. It works by detecting objects and remembering their positions. So, it needs landmark features, like trees, slides, and flowerbeds, to know its relative location. If the lawn is straight without any landmarks, it won’t be able to know its position. Secondly, processing a large lawn is also difficult, as too many obstacles put a burden on the processor. It is suitable for medium-sized lawns up to 1.5 acres.

LiDAR has the shortest range compared to the other two. Typically, the sensor’s range is only 30 to 40 meters. It can’t work in fields where objects are far away. It’s an ideal option for small to medium densely populated lawns.

Cost

Vision technology is the cheapest of all. That’s the reason it is even available in entry-level robotic lawnmowers. The cameras used in the mower are inexpensive. Instead, the software used to identify objects and process the images is more costly. Overall, this technology is affordable.

Then, we have NRTK. Usually, lawnmower robots rely on RTK. But the recent and advanced ones use NRTK for better performance. The cost is more than vision technology.

LiDAR is also an expensive technology. It uses high-end components, especially the LiDAR sensor. It actually makes the robotic mower capable of handling the lawn on its own. That’s why it comes with a higher price tag.

Weaknesses and Limitations

NRTK is much better than RTK, but it still has some weaknesses and limitations. The first limitation is that it needs a clear sky to work. If there are tall buildings and high walls nearby, its performance can be affected due to signal blockage. Similarly, the tree is too dense; it can also stop the signals.

LiDAR cannot work in an open field. It needs obstacles around so that the pulsed laser can bounce back, and it can calculate the distance. It gets lost if there is no obstacle within 30 meters.

Secondly, it is weather-sensitive. If there are heavy particles, like steam, fog, and thick dust, the laser beams scatter and prevent the mower from finding the accurate positions of the obstacles around. Moreover, it can’t accurately detect reflecting surfaces, such as glass doors and metal fences. It happens due to the reflection of the laser pulses.

The primary weaknesses of Vision navigation are lighting and optical illusions. Its vision doesn’t work in the dark. It needs proper lighting to see the lawn, which is why it can only work in the day. Some mowers even consider shadows as holes. So, it’s a challenge, and advanced AI identification is needed.

Moreover, if the area has no obstacles and the lawn is plain, it will struggle to find objects and their relative position. And if something blocks the lens, like a grass clipping or a water splash, the robot mower will have no clue of the surroundings.

Which Navigation Technology is You Need for Your Lawn?

A Complex Lawn

A complex lawn means the lawn has many obstacles, such as multiple flowerbeds, playground sets, side passages, trees, furniture sets, etc. On top of that, children’s toys and other items that do not belong on your lawn.

In such a complex lawn, LiDAR can perform well. It can instantly detect objects and dodge them. Not only does it avoid them but also mows around their edges with incredible accuracy.

Large Plain Lawn

For large and plain lawns where you don’t have trees and other obstacles in the middle, the right option is NRTK. The reason is that Vision and LiDAR need obstacles around to know their relative position. Otherwise, they have no clue where to go.

Thus, having NRTK is essential. It will help the mower with its absolute positioning through accurate coordinates. So, no matter how large the lawn is, the mower will mow it without any physical boundaries or wires. Any open field with an area of 1 acre or above with few trees and obstacles, NRTK is much needed.

A Simple Backyard

If you have a small and simple backyard, like a flat rectangle with no obstacles, you can go with the vision tech. It will be an affordable option and is suitable for small and simple lawns. It can easily identify simple objects in the lawn and dodge them. It won’t be as accurate as LiDAR, but it will do the job.

Lawn with Trees and Between Buildings

If your lawn has trees or heavily dense canopies along with high-rise buildings around, you need a hybrid robotic lawn mower.

You can opt for LiDAR or Vision, but if you can go with a hybrid, that will be a better option. It will detect obstacles more accurately.

Why Choose One? Go with a Mower that Combines All

Each navigation technology has some limitations and weaknesses. They fail in certain terrains and scenarios. That’s the reason relying on one navigation technology isn’t a good idea.

If you are looking for a robotic lawnmower that can put lawn care on automation, you need a combination of LiDAR, NRTK, and Vision navigation. The output of all these technologies will assist the mower in processing the right information and navigating any kind of lawn.

If one fails or has any issues, the other two are there to guide the mower. For future-proofing and getting a lawnmower that doesn’t need human intervention, it’s a must to benefit from the three navigation technologies.

FAQs

Which is better RTK or LiDAR lawn mower?

RTK shows the absolute positioning of the mower. It helps the mower to navigate within its boundary. It has nothing to do with obstacle detection. On the other hand, LiDAR cannot provide the absolute position coordinates. It creates a 3D map through laser pulses and is excellent for handling obstacles. Both serve different purposes, so having both in the robot lawnmower would be beneficial.

Is LiDAR better than vision?

Yes, LiDAR is better than vision because it can accurately detect objects. It offers centimeter-level accuracy. It uses laser pulses and instantly detects obstacles. And it can even operate in low-light conditions. On the other hand, vision navigation could be less accurate as it depends on the imagery and pixels. It’s only better at identifying objects.

What is the difference between LiDAR and GNSS?

LiDAR uses a pulsed laser to detect obstacles around. It creates a 3D map of the lawn to help the mower navigate. In contrast, GNSS relies on satellites to determine the absolute position of the mower. It helps the mower understand its boundaries and navigate within the lawn.

What can LiDAR not detect?

LiDAR cannot detect transparent surfaces (glass), highly reflective surfaces (mirrors), and very thin and small objects. Secondly, it struggles due to the weather. It cannot work in heavy fog and with dense dust particles in the air.