The field of LiDAR odometry and sensor fusion is rapidly advancing, with a focus on improving robustness and accuracy in diverse environmental conditions. Recent developments have centered on enhancing the performance of LiDAR odometry models in adverse weather conditions, such as snowfall, and improving the efficiency of training processes. Additionally, there is a growing interest in developing open-source frameworks and software tools to support LiDAR research and applications. Noteworthy papers in this area include: Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions, which presents an unsupervised LiDAR odometry model that can effectively generalize across different weather conditions. Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy, which proposes a compact octo-voxel-based map structure for efficient and accurate LiDAR-inertial odometry. DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion, which introduces a novel visual-LiDAR odometry framework that leverages sparse spatial-temporal fusion to enhance accuracy and robustness. SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton, which proposes a novel ICP algorithm with uncertainty estimation for accurate pose estimation and consistent noise parameter inference. A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling, which presents a robust LiDAR-inertial odometry system that does not rely on sensor-specific modeling. Multi-LVI-SAM: A Robust LiDAR-Visual-Inertial Odometry for Multiple Fisheye Cameras, which proposes a multi-camera LiDAR-visual-inertial odometry framework for highly accurate and robust state estimation. LiGuard: A Streamlined Open-Source Framework for Rapid & Interactive Lidar Research, which presents an open-source software framework for rapid development and interactive research on LiDAR-based projects.