Simultaneous Localization and Mapping (SLAM)
Definition
SLAM combines creating an environment map with determining one’s own position.
SLAM refers to methods by which a mobile system simultaneously creates a map of its environment and estimates its own position within it.
Simply explained
A robot explores an unknown room and simultaneously tries to figure out where it is inside it.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A warehouse robot maps aisles with camera and lidar and localizes itself during travel.
Limits and pitfalls
- Sensor errors, moving objects, and loop closure can distort maps; safe operation requires additional controls.
Origin & history
The research field originated in robotics in the 1980s and developed probabilistic as well as graph-based methods.
Use in mid-sized companies
- Possible application: A warehouse robot maps aisles using camera and lidar and localizes itself while driving.
Frequently asked questions
What does Simultaneous Localization and Mapping (SLAM) mean?
SLAM refers to methods by which a mobile system simultaneously creates a map of its environment and estimates its own position within it. What should be considered? Sensor errors, moving objects, and loop closure can distort maps; safe operation requires additional checks.
Sources
- Simultaneous Localization and Mapping: Part I · Durrant-Whyte and Bailey, Simultaneous Localization and Mapping: Part I
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
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