SLAM: Simultaneous Localization and Mapping

SLAM (Simultaneous Localization and Mapping) is the technology that lets a robot build a map of an unknown environment while walking - and know exactly where on that map it currently is. With no GPS available, the robot does it all with its own sensors.

What problem it solves

To run autonomously in factories, underground garages and malls, a mobile robot must answer three questions: Where am I, what is around me, and how do I get there. SLAM handles the first two: LiDAR/camera scans the environment, the algorithm builds an occupancy grid map in real time and marks the robot current pose on it. With map + localization, path planning (the third question) has a foundation.

A robot without SLAM can only follow a fixed trajectory (like a magnetic-strip AGV); changing a route means re-laying the strip. A robot with SLAM maps once, then navigates freely on the map - and remaps whenever the layout changes.

How to read the key specs

Mapping accuracy: how far the map deviates from reality. Commercial chassis usually need centimeter-level; docking tasks also require repeatability.
Loop closure: whether the robot recognizes "I have been here" when returning to start after a loop - it directly decides whether long corridors and big factories suffer map drift.
Localization rate and latency: the faster the update, the quicker dynamic obstacle response; usually 10 Hz or above.
Sensor type: a 2D single-line LiDAR only builds a flat map - it misses hanging signs and low obstacles. A 3D multi-line LiDAR builds 3D space and solves obstacle avoidance and anti-drop together.

Common misconceptions

Misconception 1: SLAM is an optional feature. It is the infrastructure of autonomous mobile robots, not a bonus; a robot without SLAM is just a remote-controlled car.

Misconception 2: more sensors mean more stable SLAM. Sensor fusion adds calibration and failure points; a single 3D multi-line LiDAR is often better on accuracy, reliability and cost indoors.

Misconception 3: only ask whether it can map. What matters more is long-range drift, dynamic environments, narrow aisles and low obstacles - that is where real-world results diverge.

Recommended models

Every Tbot chassis ships with 3D multi-line LiDAR SLAM navigation: Tbot-N5-3D (six-wheel mid-drive differential, 100-500kg payload), Tbot-OW (omni wheels, strafe without turning), Tbot-Clean-U75 (underground garage cleaning). Full parameters in the full spec table.

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SLAM: Simultaneous Localization and Mapping

What problem it solves

How to read the key specs

Common misconceptions

Recommended models

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