In an era where technology is infiltrating nearly every corner of life, private gardens remain one of the few frontiers largely untouched.

For years, the rules of lawn care were written by fuel and sweat. When robotic lawn mowers first entered the scene, they were more tedious to master than convenient. Users had to bury boundary wires around their lawns before the robots could operate. The process was cumbersome, and any broken wire meant more work. Even models using RTK (real-time kinematic) positioning had limits: once they passed under a tree canopy or near a wall, signal loss caused them to freeze or wander aimlessly.

Demand clearly existed, but no product could meet it.

That is now changing. Recently, Frost & Sullivan certified Dreame’s robotic lawn mower as having the highest cumulative global sales among LiDAR (light detection and ranging) models, spotlighting both the brand and the technology driving this category.

At first glance, it looks like a single Chinese company winning in the global market. But the deeper message is that in complex outdoor environments where lighting and terrain may vary widely, LiDAR has moved from experimentation to commercial maturity.

It recalls a similar shift in the automotive sector. Once seen as a luxury feature for autonomous vehicles, LiDAR is now standard in high-end cars. A comparable evolution is unfolding in the backyard, as LiDAR becomes the defining technology of premium robotic lawn mowers.

So why has LiDAR become the key variable in reshaping the garden economy, and what did Dreame get right?

What LiDAR represents

It all begins with a shift in perception.

Across Europe and North America, lawn maintenance has long been more than routine. It’s also a marker of pride and social status. That sentiment supports a sizable market. According to Statista, the global lawn and garden market is projected to reach USD 360.6 billion by 2025, while Grand View Research estimates that lawn mowers will generate about USD 35.3 billion in the same period.

Before LiDAR arrived, this market was dominated by fuel-powered machines and wire-guided robots. The user experience stagnated, much like the mobile phone industry before smartphones, stable but uninspired.

Dreame’s success lies in transforming lawn mowers to handle outdoor complexity with the same intelligence that defines modern home robotics.

Where consumers once paid for mechanical performance, they now expect intuitive, seamless operation, the same convenience delivered by robotic vacuum cleaners indoors.

In this new landscape, ease of use is the true selling point. Brands are competing to eliminate setup hassles, reduce user intervention, and maximize value. Achieving that transformation requires a technological leap.

How LiDAR solved the garden’s toughest problem

Outdoor environments are unstructured by nature. Lighting changes drastically between noon and dusk. Lawns vary from flat to sloped, and dense vegetation or nearby buildings can disrupt GPS signals. Vision-based systems falter in glare or shadows, while RTK systems behave like “novice drivers” overly dependent on navigation, stalling the moment the signal drops.

These limitations meant users had to adapt to the robot, not the other way around. Dreame tackled that problem directly.

LiDAR gives robots vision at night. Unlike cameras, it doesn’t rely on ambient light. It emits laser pulses to map surroundings with centimeter-level precision and detection ranges of up to 70 meters in any lighting condition.

By adapting SLAM (simultaneous localization and mapping) algorithms originally developed for indoor robotic vacuums, Dreame achieved technology transfer, enabling its mowers to navigate freely without physical boundaries or base stations.

Graphic source: Dreame.

From seeing to understanding the environment

Dreame’s appeal lies in translating advanced technology into intuitive simplicity and removing the psychological barriers that once kept consumers away.

In consumer electronics, there’s an unspoken rule: sophistication must serve usability. Users don’t care about sensor line counts, they just want mowing to be effortless.

Older models required homeowners to hire technicians to bury boundary wires, often at high cost. Imagine buying a smartphone and paying extra for someone to install cables throughout your home. That was the norm for lawn mowers until Dreame eliminated it with LiDAR.

By relying solely on LiDAR for high-precision mapping, positioning, and obstacle avoidance, Dreame removed the need for complicated setup.

A visual overview of the Dreame A2’s environmental mapping system. Image source: Dreame.

For distributors, that also meant fewer headaches: no complaints about broken wires, no blind spots, and fewer returns. These advantages helped Dreame climb to the top of the global market during this year’s fall sales season.

Navigation is only the first step; true success requires adaptability.

A single sensor, limited by what it detects, cannot perceive the full picture. To a simple laser scanner, a rock and a sleeping dog might appear identical. That is why competition has shifted from hardware specifications to multi-sensor fusion algorithms.

Dreame’s A3 AWD Pro embodies that evolution. As the world’s first four-wheel-drive robotic lawn mower equipped with 360-degree 3D LiDAR and binocular vision, it debuted at the IFA trade show this year, demonstrating up to 98% accuracy in dynamic obstacle recognition.

Think of it as giving the robot a professional driver’s brain: LiDAR provides the structure, binocular vision adds detail, and artificial intelligence supplies context and meaning.

Premium users value that sophistication. Most machines can handle flat terrain, but few perform well on slopes and uneven ground. In Europe’s hilly backyards, that’s the ultimate engineering test. Dreame’s four-wheel hub motors allow an 80% climbing gradient, and its offset blade trims within three centimeters of edges. The company’s strength lies not in pricing, but in tailoring technology to real-world terrain.

A closer look at Dreame’s strategy shows its success is no accident.

At the core of its lawn mowers is an advantage in algorithms. Unlike traditional garden toolmakers, Dreame’s DNA is rooted in technology. Its expertise in indoor robotics provided a strong foundation. By transferring SLAM algorithms and motion control logic from vacuums to lawn mowers, Dreame bypassed years of trial and error.

While legacy brands focused on motor stability, Dreame entered with algorithms refined through millions of indoor tests.

From its sensor fusion system to adaptive obstacle avoidance, Dreame applies precision robotics know-how to outmaneuver traditional equipment makers. This cross-domain reuse reduces R&D costs and enables rapid iteration and over-the-air software updates.

LiDAR-powered lawn mowers now mirror the trajectory of Chinese electric vehicles and drones abroad. Dreame is advancing solid-state LiDAR research and developing broader multi-sensor fusion frameworks, adapting and applying principles from autonomous driving.

In Dreame’s vision, future lawn mowers will go beyond cutting grass. They will integrate with smart home systems, adapt to environmental changes, and respond autonomously. Behind that ambition lies China’s strength in algorithms and supply chains, combining to power the next wave of precision robotics.

North America remains one of the few frontiers in the garden economy yet to be conquered, characterized by larger lawns, tougher terrains, and thicker grass. Dreame sees its four-wheel-drive system and dual-vision fusion algorithms as its main advantages in tackling this market.

But global success also requires localization. Dreame’s expansion strategy depends not only on e-commerce, but also on local dealer networks that allow customers to see, test, and service machines close to home. That physical presence has been vital to its global market share and brand recognition.

As the second wave of smart hardware takes shape, user experience driven by genuine innovation will determine which brands stand out.

KrASIA Connection features translated and adapted content that was originally published by 36Kr. This article was written by Xiao Xi for 36Kr.