Drift Free

Drift-free localization, crucial for autonomous systems operating in GPS-denied environments, aims to accurately estimate and maintain a robot's position and orientation without accumulating errors over time. Current research focuses on integrating diverse sensor data (LiDAR, IMU, UWB, cameras) using advanced algorithms like Kalman filtering, factor graph optimization, and deep learning for robust state estimation and outlier rejection, often incorporating map information or learned models for improved accuracy. These advancements are significantly impacting robotics, enabling reliable navigation for applications such as autonomous vehicles, drones, and mobile robots in challenging indoor and outdoor settings.