Localization Algorithm

Localization algorithms aim to determine the precise position and orientation of an object or sensor, a crucial task across diverse fields like robotics, autonomous driving, and surveillance. Current research emphasizes robust solutions handling noisy or incomplete data, focusing on techniques like Bayesian estimation, Kalman filtering (with extensions such as adaptive noise tuning and outlier detection), and neural networks (including vision transformers and shallow networks). These advancements improve accuracy and efficiency in challenging environments (e.g., indoors, in rain, or with limited sensor data), impacting applications ranging from indoor navigation to precise 3D mapping and object tracking.