LiDAR IMU
LiDAR-IMU fusion integrates data from Light Detection and Ranging (LiDAR) sensors and Inertial Measurement Units (IMUs) to achieve robust and accurate 3D localization and mapping. Current research emphasizes developing tightly-coupled methods, often employing Kalman filtering variants like MSCKF or iterative point-level undistortion techniques, to improve accuracy and efficiency, particularly in dynamic environments. Focus areas include efficient data association strategies, robust calibration techniques (including targetless methods addressing limited robot motion), and the development of globally consistent mapping frameworks. These advancements are crucial for applications such as autonomous navigation, robotics, and motion capture, enabling more reliable and precise pose estimation in challenging scenarios.