The field of sensor fusion and state estimation is witnessing significant developments, driven by the need for accurate and robust localization and tracking in various environments. Researchers are exploring innovative methods to combine data from different sensors, such as radar, LiDAR, GPS, and inertial measurement units, to improve the accuracy and reliability of state estimation. Noteworthy papers in this area include:

• IMU-Preintegrated Radar Factors for Asynchronous Radar-LiDAR-Inertial SLAM, which introduces a novel method to reduce computational costs in radar-LiDAR-inertial SLAM systems.
• High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration, which proposes a multi-sensor fusion system for high-precision localization of climbing robots.
• S$^3$E: Self-Supervised State Estimation for Radar-Inertial System, which presents a self-supervised state estimator that fuses radar signal spectra and inertial data for accurate localization.
• Radio-based Multi-Robot Odometry and Relative Localization, which proposes a multi-robot UGV-UAV localization system using radio-based methods.
• Two stage GNSS outlier detection for factor graph optimization based GNSS-RTK/INS/odometer fusion, which introduces a two-stage outlier detection method to improve the robustness of GNSS-RTK positioning.
• Trajectory Based Observer Design: A Framework for Lightweight Sensor Fusion, which proposes an optimization-based methodology for designing observers for general nonlinear systems and multi-sensor setups.
• A Model-Based Extended State Observer for Discrete-Time Linear Multivariable Systems, which presents a model-based extended state observer for discrete-time linear multivariable systems.