Localization

Localization is the process by which an autonomous system determines its position within a given map or environment. This involves estimating the robot's pose, which includes its position and orientation, using various sensors such as GPS, IMU (Inertial Measurement Unit), and visual inputs. Techniques for localization can be categorized into two main approaches: absolute localization, which relies on external references (e.g., GPS), and relative localization, which uses odometry and sensor data to infer position changes over time. Algorithms such as Kalman filters, particle filters, and simultaneous localization and mapping (SLAM) are commonly employed to fuse sensor data and improve accuracy. The mathematical principles underlying these techniques often involve probabilistic models and Bayesian inference, allowing for the estimation of uncertainty in the robot's position. Accurate localization is fundamental for effective navigation and task execution in robotics.