odometry

Estimating a robot’s change in position and orientation over time by integrating motion measurements (e.g., wheel encoders, IMU data, joint encoders). Odometry provides short‑term pose updates but accumulates drift from sensor noise, slippage, and modeling errors. Visual methods (visual odometry / visual-inertial fusion) or external references (GPS, landmarks, fiducials, SLAM) are commonly used to detect and correct this drift, yielding more accurate and globally consistent localization. Key points: incremental pose estimate, susceptible to cumulative error, improved by sensor fusion and loop closure.