The field of localization and mapping is experiencing significant advancements, driven by the need for accurate and robust positioning systems in various applications, including autonomous vehicles, mobile mapping, and smart factories. Recent developments focus on improving the accuracy, efficiency, and adaptability of localization algorithms, as well as the creation of comprehensive datasets for testing and validation. Notable innovations include the integration of multi-sensor fusion, adaptive communication-computation codesign, and dynamic-aware mapping frameworks. These advancements aim to address the challenges posed by complex environments, such as urban areas, tunnels, and dynamic scenarios. The development of large-scale datasets and benchmarking frameworks is also crucial for evaluating the performance of localization systems and driving further innovation. Some noteworthy papers in this area include: ACCESS-AV, which proposes an energy-efficient Vehicle-to-Infrastructure localization framework, and Uni-Mapper, which presents a unified mapping framework for multi-modal LiDAR systems. Additionally, DuLoc introduces a robust and accurate localization method that couples LiDAR-inertial odometry with offline map-based localization, demonstrating improved performance in changing and dynamic environments.