SLAM (Simultaneous Localization And Mapping) is the technique that lets a robot build a map of an unknown location while pinpointing its own position within it in real time, using data from its sensors (LiDAR, cameras). It is the navigation technology that makes AMRs autonomous, with no infrastructure on the ground.
SLAM: how it works and real-world examples
In practice, a robot equipped with SLAM scans its surroundings with one or more sensors, advances in small increments and constantly recalibrates its position relative to landmarks already mapped (walls, racking, columns). This dual operation — mapping and localizing at the same time, with no pre-existing map or indoor GPS — is what distinguishes SLAM from simple guided-path tracking such as wire guidance.
The material-handling robot market illustrates this technology well. The autonomous pallet truck navigates by fusing “LiDAR-SLAM 3D and VSLAM” (visual SLAM using cameras), a multi-sensor approach that lets it find its way with no infrastructure and handle transfers between floors via elevators. The MiR1200 Pallet Jack also combines SLAM and artificial intelligence to recognize pallets; the STILL AXL 15 iGo uses a “3D visual SLAM” paired with a LiDAR to load trucks with no infrastructure; VisionNav combines 3D SLAM and deep learning for precise multi-level stacking.
SLAM is not specific to logistics: it's the same family of algorithms used by household robot vacuums or self-driving vehicles, adapted here to the scale and precision requirements of a warehouse.
A practical benefit of SLAM for an operator: the site map is updated by simply re-running a mapping pass, with no physical intervention. Moving a drop-off point, adding a zone or reconfiguring an aisle becomes a software operation, compared with the work required to modify a wire-guided circuit or reposition laser reflectors.
The robot combines SLAM and 3D LiDAR to navigate with no infrastructure. Learn more about the robot.
FAQ
What does SLAM concretely mean for a warehouse robot?
Concretely, the robot scans its environment with a LiDAR and/or cameras, builds a map as it moves, and recalibrates its position on every pass. Once the map has stabilized, it calculates its routes and avoids obstacles without human intervention or floor markings.
Does SLAM replace LiDAR?
No: they are two complementary elements. LiDAR is the sensor that measures distances using laser; SLAM is the algorithm that processes these measurements, often combined with cameras, to map the site and localize the robot in real time.
Related terms
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