Occupancy grid mapping refers to algorithms that generate maps from noisy sensor data, representing the environment as a grid of binary variables indicating obstacle presence. It computes estimates of these variables while accounting for uncertainty in robot pose. Major components include interpreting sensor data, integrating measurements over time, estimating robot position, and strategies for exploration. Common issues include representing the continuous environment with a discrete grid and dealing with noise in sensor data and pose estimates.

1. What is an occupancy grid and how can it be used? What are some of the problems that typically
occur in creating it?
Solution
Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for
mobile robots which address the problem of generating maps from noisy and uncertain sensor
measurement data, with the assumption that the robot pose is known.
The basic idea of the occupancy grid is to represent a map of the environment as an evenly
spaced field of binary random variables each representing the presence of an obstacle at that
location in the environment. Occupancy grid algorithms compute approximate posterior
estimates for these random variables.
There are four major components of occupancy grid mapping approach. They are:
Interpretation,
Integration,
Position estimation, and
Exploration.
Occupancy grid algorithms represent the map as a fine-grained grid over the continuous space of
locations in the environment. The most common type of occupancy grid maps are 2d maps that
describe a slice of the 3d world