Robot vacuum navigation types: LiDAR, SLAM, vSLAM and more

When evaluating a robot vacuum, it’s natural to focus on features that directly impact cleaning, like suction power, cleaning modes, and how much dirt and dust it can hold before you have to empty it. But if you want to get an idea of how well a particular product will clean your home, it’s important to also consider how it maps and navigates environments.

Mapping and navigation ultimately determine how efficiently and thoroughly a robot vacuum cleans. Effective navigation allows robot vacuums to maneuver through various obstacles, traverse different floor types, and cover the entire cleaning area without missing spots. It also contributes to shorter cleaning times and longer battery life by optimizing cleaning paths and avoiding unnecessary retracing.

This feature is part of TechHive’s in-depth coverage of the best robot vacuums.

Accurate navigation enhances the overall user experience, as well, providing convenience and confidence to users who rely on these smart appliances to maintain their living spaces.

Robot vacuum navigation types explained

Here we’ll look at the different ways robot vacuums map and navigate your home, ranging from the most primitive to the most advanced approaches.

These technologies aren’t mutually exclusive and are often used in various combinations to improve navigation, but it’s important to understand how each works on its own to make the most informed purchasing decision.

Sensor-based navigation

Pros

• Budget-friendly
• Simple operation
• Suitable for small spaces
• Few, if any, privacy concerns

Cons

• Limited navigation precision
• Random cleaning patterns
• Difficulty with complex environments
• Lower cleaning efficiency

Sensor mapping is the earliest and most basic navigation method used by robot vacuums. It employs an array of sensors to tell the robot its position on the floor and identify any safety issues.

All robot vacuums, regardless of price, incorporate an array of sensors to help them find their way around your home, but budget robot vacuums often rely exclusively on this type of navigation.

The most common sensors you’ll find on any robot vacuum include:

• Cliff sensors: These enable the robot vacuum to identify stairs, ledges, and steep thresholds so they don’t fall or tip over. Cliff sensors on the base of the robot bounce infrared light beams off the floor to calculate the distance between the two. The longer it takes the light to return to the sensor, the longer the fall distance. If a significant drop is detected, the robot will stop and reverse direction.
• Wall sensors: Wall sensors help the robot vacuum locate and navigate along walls. This helps prevent collisions but also allows them to follow wall edges—where dirt typically accumulates—for a more thorough cleaning.
• Bump sensors: Bump sensors allow robot vacuums to detect physical contact with obstacles. The most basic form is physical bumpers or touch-sensitive pads located around the perimeter of the device. When the vacuum makes contact with an obstacle, such as a wall or furniture leg, these bumpers depress a trigger, signaling to the vacuum that it has encountered an obstruction. More advanced robot vacuums may use other technologies, such as infrared light or software algorithms, for obstacle detection.
• Optical encoders: These sensors are positioned on the vacuum’s wheels to track the distance traveled by the robot. They use light sensors to count wheel rotations. By monitoring wheel movement, the robot can accurately calculate its travel distance, which helps with navigation, route planning, and timely return to the charging base when needed.
• Battery sensor: Robot vacuums need to periodically return to their charging base when their battery is depleted. To do so, they must detect when their battery is low and accurately gauge the distance traveled from the base to ensure they can return within the remaining charge. Battery sensors play a critical role in monitoring and managing the battery charge and providing information about the vacuum’s distance from the charging dock.

Sensor-based robot vacuums offer an affordable and straightforward cleaning solution for budget-conscious users. Even with the most basic sensors like those described above, these models can effectively navigate around obstacles and prevent falls, making them suitable for small spaces with simple layouts.

However, their navigation precision of sensor-only bots may be limited, resulting in random cleaning patterns and potential inefficiencies such as long cleaning times and missed spots.

Sensor-based vacuums may also struggle in complex environments without the advanced smart features found in higher-end models.

While sensor-dependent vacuums are affordable, require less maintenance, and are easy to operate, users should consider their cleaning efficiency and suitability for their specific home environment before opting for a sensor-based robot vacuum.

Camera mapping

Pros

• Precise navigation
• Efficient cleaning patterns
• Advanced smart features
• Remote monitoring

Cons

• Limited functionality in low light
• Dependency on visual input
• Privacy concerns
• Cost

Some robot vacuums utilize a built-in digital camera for navigation, allowing them to “see” the environment more like a human does. These cameras are often paired with SLAM algorithms or LIDAR (more on those in a moment) for greater navigation accuracy.

Cameras enhance robot vacuum functionality and user experience in several ways. Primarily, they provide greater navigation precision than sensors alone, because they capture and analyze visual data to better understand cleaning areas and navigate around obstacles more accurately.

iRobot

iRobot

iRobot

They also enable advanced smart features, such as object recognition and room mapping. Some camera-equipped robot vacuums, for instance, can recognize and avoid specific objects, such as pet bowls or cables, increasing safety and efficiency. Room mapping capabilities allow for customized cleaning schedules and targeted cleaning of specific areas.

Additionally, some models offer remote monitoring capabilities, allowing users to view live video feeds of their homes and use the vacuum as a surveillance device for added security.

However, camera-mapping robot vacuums come with some drawbacks. Just as humans struggle to see in the dark, camera-dependent robots face similar challenges, limiting their ability to navigate in low-light conditions unless equipped with supplementary lighting. These robots may also misinterpret dark tiles, carpets, and deep shadows cast by furniture as drop-offs and avoid navigating over these areas.

Robot vacuums equipped with digital cameras raise several privacy concerns as well, including the potential for unauthorized surveillance, data security breaches, and third-party access to captured images or videos.

These fears aren’t unfounded. In 2022, Roomba robot vacuums captured intimate images of home occupants that were later leaked to social media, although Roomba manufacturer iRobot said the robot vacuums in question were test models, not consumer units.

Captured images may also inadvertently reveal details about the home’s layout or personal information, posing risks to privacy and security. Users should carefully review privacy policies, disable camera or microphone functionalities if unnecessary, and take steps to secure their home network to mitigate these risks.

SLAM navigation

Pros

• Accurate navigation
• Adaptability to complex environments
• Real-time mapping
• Optimized cleaning paths

Cons

• Cost
• Power consumption
• Mapping errors
• Potential privacy concerns

SLAM (Simultaneous Localization and Mapping) is an algorithmic technique robot vacuums use to create maps of their surroundings while simultaneously determining their position within those maps. It works by collecting data from cameras, LiDAR, wheel encoders, and other sensors to identify features in the environment.

Based on this data the robot then initializes a map—often visually displayed in the vacuum’s companion app—and continuously updates it as it moves, refining its estimated position through probabilistic localization methods.

iRobot

iRobot

iRobot

SLAM algorithms also identify and correct errors by detecting loop closures—instances where the robot revisits a previously visited location—to refine maps and improve consistency. Overall, SLAM enables robots to navigate and map complex environments autonomously and accurately.

SLAM offers several advantages over other mapping methods, including precise navigation, adaptability to complex environments, real-time mapping capabilities, and optimized cleaning paths.

In a blind test, you’d be able to tell a SLAM robot vacuum from a purely sensor-based one by its movement; rather than ping-ponging from one obstacle to the next, a SLAM robot will move in logical lines and navigate around edges and corners with ease. For users, that translates to reduced cleaning times and better overall coverage.

The downside of SLAM technology is that it requires advanced sensors and computational power, which increases the cost of robot vacuums that use it. SLAM may also be susceptible to mapping errors in environments with reflective surfaces or low-light conditions, higher power consumption, and increased complexity leading to potential maintenance issues.

Because SLAM technology involves mapping the interior of homes, there’s a possibility that sensitive information about the layout and contents of the living space could be captured. This raises concerns about privacy violations, especially if this data is accessed or shared without the user’s consent. If the robot vacuum is also equipped with cameras for vSLAM (visual SLAM), there may be additional privacy risks related to video surveillance and potential data breaches.

LiDAR navigation

Pros

• Accurate navigation
• Adaptability to complex environments
• Real-time mapping
• Optimized cleaning paths

Cons

• Cost
• Power consumption
• Mapping errors
• Potential privacy concerns

LiDAR (Light Detection and Ranging) is a sensing technology that employs laser light to gauge distances to objects in the surrounding environment. It operates by emitting invisible laser beams in various directions, which bounce off objects, such as walls, furniture, or obstacles, and return to the sensor.

By measuring the time taken for these beams to travel, LiDAR calculates distances to create a three-dimensional map of the environment. This data aids the robot vacuum in navigation, allowing it to efficiently maneuver through spaces, avoid obstacles, and plan optimal cleaning paths.

Dreame Technology

Dreame Technology

Dreame Technology

LiDAR navigation offers several advantages over other mapping methods. For starters, LiDAR sensors provide highly accurate distance measurements, enabling precise mapping of the environment and accurate localization of the robot. They are also less affected by changes in lighting conditions compared to other types of sensors, making them suitable for use in a variety of environments.

Another strength of LiDAR is its wide field of view, which allows it to capture a broad range of environmental data in a single scan, reducing the need for the robot to make frequent scanning movements or rotations to capture data from different angles.

This comprehensive coverage enables more efficient navigation, better obstacle avoidance, and faster mapping processes. Because of these benefits, LiDAR is typically used by the newest and most advanced robot vacuums on the market.

But like all navigation methods, LiDAR has its limitations. LiDAR sensors may struggle to detect certain materials like glass or transparent surfaces, leading to potential navigation challenges in environments with these materials.

Moreover, they require significant power to operate, which can impact the overall power consumption and battery life of the robot vacuum. LiDAR sensors are also relatively expensive compared to other navigation sensors, which can drive up the cost of products that use them.

Finally, LiDAR is subject to the same privacy issues around home mapping as SLAM.

Which type of robot navigation is right for you?

Selecting the right robot vacuum for your needs involves considering several factors, including cost, the complexity of your home, and your desired cleaning speed.

If budget is a primary concern, a sensor-based robot vacuum may be the best option, assuming you can tolerate the trade-off for less precise navigation.

For those with larger or more intricate living spaces, investing in a robot vacuum equipped with advanced navigation technologies such as SLAM, LIDAR, or camera-based navigation can pay off with greater efficiency and coverage.

Check out our top picks for robot vacuums

Additionally, if you prioritize quick cleaning sessions, choosing a robot vacuum with faster navigation speeds and optimized cleaning paths can help minimize cleaning time.

If you weigh these considerations alongside your specific preferences and requirements, you can confidently select the robot vacuum that best suits your home and lifestyle.