How SLAM is changing the game for mobile robots and autonomous vehicles

Robots and self-driving cars use simultaneous localization and mapping (SLAM) to make a map of their surroundings and figure out where they are in that map at the same time. It is a key skill for any mobile robot or self-driving car that needs to move around and work in environments that are unknown or change.

In SLAM, the robot or vehicle uses sensors such as lasers, cameras, or inertial measurement units (IMUs) to gather data about its surroundings as it moves. This information is used to make a map of the environment and figure out where the robot is in it. The process of creating the map and determining the robot's location is known as "localization."

There are two main approaches to SLAM: extended Kalman filter (EKF) SLAM and graph-based SLAM. EKF SLAM uses a Kalman filter to estimate the robot's pose (i.e., position and orientation) and to build a map of the environment. The Kalman filter uses information from the robot's sensors and motion models to make a rough estimate of where the robot is and where it is on the map. In graph-based SLAM, the robot's pose and the map are represented as a graph, with nodes representing the robot's poses and edges representing the relationships between poses. As the robot moves and learns more about its surroundings, the graph is slowly built up and improved.

SLAM is hard because the robot has to figure out both where it is and where it is on the map at the same time. It also has to deal with uncertainty, noise, and other sources of error in the data. It also takes a lot of computing power because the robot has to process and combine data from its sensors and motion models all the time.

SLAM has many uses in robotics, such as in self-driving cars, robots that search and rescue people, and service robots. It is also used in augmented reality, where it can be used to build a map of the real world and to determine the user's location within that map.