What is Slam?

Below just an outline, but something I am investigating,  detail at the link.

The definitive guide to SLAM & mobile mapping

SLAM 101   ....  How does SLAM work  ....

How does it work, and what does it mean for mobile 3D mapping?

SLAM 101

Simultaneous localization and mapping (SLAM) is not a specific software application, or even one single algorithm. SLAM is a broad term for a technological process, developed in the 1980s, that enabled robots to navigate autonomously through new environments without a map.

Autonomous navigation requires locating the machine in the environment while simultaneously generating a map of that environment. It’s very difficult to accomplish, because the machine needs to have a map of the environment to estimate its own location. But to generate the map, it needs to know its own location.

As a result of this never-ending circle of dependencies, SLAM was sometimes called a “chicken or egg” problem.

How does SLAM work?

There are many approaches to SLAM. Luckily, we can still make some generalizations to demonstrate the basic idea.

Here’s a very simplified explanation: When the robot starts up, the SLAM technology fuses data from the robot’s onboard sensors, and then processes it using computer vision algorithms to “recognize” features in the surrounding environment. This enables the SLAM to build a rough map, as well as make an initial estimate of the robot's position.

When the robot moves, the SLAM takes that initial position estimate, collects new data from the system’s on-board sensors, and makes a new (and improved) position estimate. Once that new position estimate is known the map is updated in turn, which completes the cycle.

By repeating these steps continuously, the SLAM tracks the robot's path as it moves through the asset. At the same time, it builds a detailed map.  ... '

SLAM and Mobile Mapping

Brought to my attention for a proposed project.    Below the intro, much more at the link, will follow with more....  Note, aspects of autonomy. 

The definitive guide to SLAM & mobile mapping

SLAM 101

Simultaneous localization and mapping (SLAM) is not a specific software application, or even one single algorithm. SLAM is a broad term for a technological process, developed in the 1980s, that enabled robots to navigate autonomously through new environments without a map.

Autonomous navigation requires locating the machine in the environment while simultaneously generating a map of that environment. It’s very difficult to accomplish, because the machine needs to have a map of the environment to estimate its own location. But to generate the map, it needs to know its own location.

How does SLAM work?

There are many approaches to SLAM. Luckily, we can still make some generalizations to demonstrate the basic idea.

Here’s a very simplified explanation: When the robot starts up, the SLAM technology fuses data from the robot’s onboard sensors, and then processes it using computer vision algorithms to “recognize” features in the surrounding environment. This enables the SLAM to build a rough map, as well as make an initial estimate of the robot's position.

When the robot moves, the SLAM takes that initial position estimate, collects new data from the system’s on-board sensors, and makes a new (and improved) position estimate. Once that new position estimate is known the map is updated in turn, which completes the cycle.

By repeating these steps continuously, the SLAM tracks the robot's path as it moves through the asset. At the same time, it builds a detailed map.   ... " 

Real-time Mapping and Localization

Much work underway in smart mapping ... Given the increasing need for real-time intelligence.   We used aspects of this in forestry asset analysis.

Collaborative simultaneous localization and mapping technique uses available Wi-Fi networks
by Ingrid Fadelli , Tech Xplore

In recent years, research teams worldwide have developed new methods for simultaneous localization and mapping (SLAM). These techniques can be used to construct or update maps of a given environment in real time, while simultaneously tracking an artificial agent or robot's location within these maps.

Most existing SLAM approaches rely heavily on the use of range-based or vision-based sensors, both to sense the environment and a robot's movements. These sensors, however, can be very expensive and typically require significant computational power to operate properly.

Aware of these limitations, researchers at the Singapore University of Technology and Design, Southwest University of Science and Technology, the University of Moratuwa and Nanyang Technological University have recently developed a new technique for collaborative SLAM that does rely on range-based or vision-based sensors. This technique, presented in a paper prepublished on arXiv, could enable more effective robot navigation within unknown indoor environments at a cost significantly lower than that of most previously proposed methods. ... "