The field of autonomous systems is witnessing significant advancements in sensor fusion and calibration, enabling more accurate and robust perception capabilities. Researchers are exploring innovative methods to integrate multiple sensors, such as LiDAR, GNSS, and IMU, to achieve reliable and high-precision mapping and localization. Notable developments include targetless extrinsic calibration techniques, which eliminate the need for artificial targets or overlapping fields of view, and resilient multi-sensor fusion frameworks that can adapt to varying environmental conditions. These advancements have far-reaching implications for various applications, including intelligent mining, smart city planning, and autonomous driving. Noteworthy papers in this area include:

• Joint Optimization-based Targetless Extrinsic Calibration for Multiple LiDARs and GNSS-Aided INS of Ground Vehicles, which presents a novel targetless extrinsic calibration method for multiple LiDAR sensors and a GNSS-aided inertial navigation system.
• Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework, which introduces a comprehensive benchmark and a resilient multi-sensor fusion framework for ground SLAM.