What’s SLAM?

In an unknown environment, SLAM is the computational problem of updating the map and simultaneously keeping track of the agents’ location within. The sensors in the robot help make up the unknown environment. Uses the robot pose estimation to improve the map landmark position estimation and vice versa.

Is SLAM necessary?

Of course! Robot models aren’t always accurate, sometimes wheel odometry error is cumulative or other IMU (Inertial Measurement Unit) sensor errors.

Input

Received

Mapping in SLAM:

Topological SLAM approaches have been used to enforce global consistency in metric SLAM algorithms the same way topological maps are a method of environment representation.

Sensing in SLAM:

Slam uses various sensors. Different types of sensors give rise to different SLAM algorithms whose assumptions of which is most appropriate to the sensors. At one extreme, laser scans or visual features provide details of a great many points within an area, sometimes rendering SLAM inference unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration. At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM. Most practical SLAM tasks fall somewhere between these visual and tactile extremes.

Types of sensors: 

Active, Passive, Intrusive

• Active: LEDs, Range Finders, Ultrasonic Sensors, Light Sensors.
• Passive: Cameras, Infrared Sensors.
• Intrusive: Markers in Augmented Reality.

Multiple Objects in SLAM:

The related problems of data association and computational complexity are among the problems yet to be fully resolved, for example, the identification of multiple confusable landmarks. A significant recent advance in the feature-based SLAM literature involved the re-examination of the probabilistic foundation for Simultaneous Localisation and Mapping (SLAM) where it was posed in terms of multi-object Bayesian filtering with random finite sets that provide superior performance to leading feature-based SLAM algorithms in challenging measurement scenarios with high false alarm rates and high missed detection rates without the need for data association.

Applications of SLAM:

• Augmented Reality
• Robotic control
• Virtual Map Building (Google Earth)
• Navigation in unknown environments