What is SLAM (Simultaneous Localization and Mapping)?
Simultaneous Localization and Mapping, commonly known as SLAM, is a fundamental algorithm enabling autonomous mobile robots (AMRs) and vehicles to simultaneously build a map of their environment and determine their own location within that map. This dual capability is essential for autonomous navigation, especially in unknown or dynamic settings.
The acronym SLAM first appeared in 1995, while the algorithms that solved this mathematical problem started seeing real-world use in the early 21st century.
Without these algorithms, robots would not be able to build maps and keep track of their location. Without SLAM, robots can not be considered autonomous.
What sensor data does SLAM rely on?
Simultaneous localization and mapping can work based on various sensor inputs such as:
- LiDAR
- Stereo cameras
- Sonar sensors
When is SLAM used?
When thinking about SLAM, people often think that it is only used in the initial mapping of an area. However, advanced SLAM algorithms can continuously update maps. This is especially useful for sites that the robot already knows but that change frequently, such as a warehouse.
Whether it’s due to pallets being moved around, corridors being blocked, or random obstacles, SLAM will allow the autonomous robots to map all of these and navigate accordingly.
