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Local mapping is a concept in robotics, particularly in relation to Simultaneous Localization and Mapping (SLAM) and Visual SLAM (vSLAM), where the robot builds a smaller, more immediate map of its surroundings, often referred to as a local map. 

The idea is to focus computational resources on understanding the robot's immediate surroundings in detail, rather than attempting to map the entire environment at once. This local map is continuously updated as the robot moves, always keeping track of the area around the robot.

Local mapping has several advantages:

# '''Efficiency''': It can be computationally expensive to constantly update and refine a map of a large environment. By focusing on a smaller local area, the robot can maintain a detailed and accurate map without overwhelming its computational resources.
# '''Relevance''': The most immediately important part of the environment for a robot is its immediate surroundings, as this is where it needs to navigate and interact. By maintaining a detailed local map, the robot can better perform tasks like obstacle avoidance, path planning, and object manipulation.
# '''Robustness''': By focusing on the local environment, the robot can be more robust to changes or disturbances in the wider environment.

In the context of rescue robots, local mapping could be crucial. For example, in a search and rescue scenario within a collapsed building, the immediate surroundings of the robot would be most relevant for identifying victims, avoiding debris and navigating tight spaces. The robot might not need a detailed map of the entire building at once, but a continuously updated local map of the area around it would be essential.