Introduction
Why Indoor Localization Matters for Mobile Robots
For indoor mobile robots, autonomous movement starts with one fundamental capability: knowing where they are.
Unlike outdoor vehicles, indoor robots cannot rely on GPS. Service robots, cleaning robots, delivery robots, and inspection robots all operate in complex, dynamic indoor environments, where walls, corridors, furniture, and people constantly change. To move safely and efficiently, robots must estimate their position in real time using onboard sensors.
This ability—commonly referred to as indoor localization—is the foundation of autonomous navigation and is a key requirement for any robot designed for real-world indoor deployment.
What Does "Autonomous Navigation" Really Mean?
In robotics, autonomous navigation is often summarized by three essential questions:
Where am I?
Where am I going?
How should I get there?
The first question—where am I—defines the localization problem. Without reliable localization, path planning and motion control become meaningless. For most indoor mobile robots, navigation is typically point-to-point: the robot must localize itself accurately, plan a path to a target position, and execute that path while responding to obstacles and disturbances.
Sensors Used for Indoor Robot Localization
The localization performance of a mobile robot depends heavily on its sensor configuration. Common sensors used in indoor environments include:
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Cameras (monocular, stereo, or depth)
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Wheel odometry
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IMU (gyroscopes and accelerometers)
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Ultrasonic and infrared sensors
In practice, no single sensor is sufficient on its own. Modern indoor mobile robots rely on sensor fusion, combining multiple data sources to balance accuracy, robustness, and cost.
Among these sensors, 2D LiDAR has become a core component for many indoor robots due to its stability, accuracy, and independence from lighting conditions.
Absolute vs. Relative Localization in Indoor Robots
Indoor robot localization methods are commonly divided into two categories: absolute localization and relative localization.
Absolute Localization
Absolute localization determines a robot's position using external references, such as:
While absolute localization can offer high accuracy, it often comes with trade-offs. Installing and maintaining infrastructure increases deployment cost, and map-matching methods can be computationally intensive. GPS, although effective outdoors, is generally unusable indoors.
Relative Localization
Relative localization estimates the robot's current position based on its movement from a known starting point. This approach typically relies on:
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Wheel odometry
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IMU measurements
Relative localization does not require external references, but its main limitation is error accumulation. Over time, small measurement errors compound, leading to drift that makes long-term accuracy difficult to maintain.
Because of these limitations, most practical indoor mobile robots combine both approaches, using relative motion estimation supported by environmental perception.
Why SLAM Is the Mainstream Solution for Indoor Mobile Robots
For robots that require a high degree of autonomy, SLAM (Simultaneous Localization and Mapping) has become the dominant approach to indoor localization.
SLAM allows a robot to:
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Build a map of an unknown environment
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Estimate its own position within that map
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Continuously update both as the robot moves
In indoor scenarios, LiDAR-based SLAM is especially popular. A 2D LiDAR continuously scans the surrounding environment, producing distance and angle measurements that form a point-based representation of nearby objects. By matching LiDAR scans over time, the robot can estimate changes in position and orientation with high reliability.
This approach enables real-time localization and navigation, even in environments where visual features are limited or lighting conditions are unstable.
From Algorithms to Real Products: SLAM in Practice
While SLAM was once mainly a research topic, it has now been widely commercialized. Many modern indoor mobile robots use 2D LiDAR combined with mature SLAM algorithms to achieve reliable autonomous movement.
In recent years, advances in sensor cost and algorithm efficiency have significantly lowered the barrier to adoption. Compact, affordable 2D LiDAR sensors now meet the performance requirements of service robots, enabling large-scale deployment without complex infrastructure.
By integrating LiDAR hardware with high-performance SLAM software, mobile robots can perform indoor localization, mapping, and path planning out of the box—making autonomous navigation practical for real-world applications such as cleaning, delivery, inspection, and human-robot interaction.
Choosing an Indoor Localization Approach for Your Robot
When selecting an indoor localization solution for a mobile robot, engineers and product teams should consider:
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Environment complexity
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Required accuracy and stability
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Cost and system integration effort
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Long-term maintenance and scalability
For most indoor mobile robots,
LiDAR-based SLAM offers the best balance between accuracy, robustness, and deployment flexibility. As the technology continues to mature, it has become the preferred choice for robots that need reliable autonomous navigation in real indoor environments.
Currently,
Slamtec's 2D LiDAR sensors are priced in the range of hundreds to thousands of RMB, making them well-suited for service robots and other indoor mobile robot applications. Despite their compact size and cost efficiency, these LiDAR sensors are able to meet the accuracy and stability requirements of real-world indoor environments.
By using 2D LiDAR as the core sensing device, combined with Slamtec's self-developed high-performance SLAM algorithms, mobile robots can achieve autonomous localization, mapping, and path planning in unknown indoor environments. This integrated approach enables reliable autonomous navigation without the need for external infrastructure, making it suitable for large-scale deployment in service robotics.
For developers and system integrators building indoor mobile robots, such SLAM-based solutions provide a practical foundation for fast integration and real-world operation.
Indoor Localization for Mobile Robots: A Practical Guide to Autonomous Navigation
Indoor Localization for Mobile Robots: A Practical Guide to Autonomous Navigation
