DUFOMap is a novel dynamic awareness mapping framework designed for efficient online processing. It identifies and classifies fully observed empty regions using ray casting, which helps determine dynamic points. DUFOMap outperforms state-of-the-art methods in terms of accuracy and computational efficiency across various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. The method is based on UFOMap and operates on point clouds processed in the voxel structure. It classifies points as static or dynamic by examining the void regions, which are areas that have been observed as empty. DUFOMap can be used for both offline map cleaning and online detection of dynamic points. It is open-source and has been validated across multiple datasets, sensors, and scenarios, demonstrating its generality, computational efficiency, and broad usability. The method is robust to sensor noise and localization errors, and it generalizes well across different scenarios and sensors. DUFOMap is compared with state-of-the-art methods such as Removert, ERASOR, OctoMap, and Dynablox, showing superior performance in terms of accuracy and computational efficiency. The method is particularly effective in handling dynamic and complex environments, and it is capable of removing dynamic points from point cloud maps with high accuracy. DUFOMap is also efficient in terms of execution time, outperforming other methods in both dense and sparse sensor settings. The method is capable of handling different sensors and scenarios, and it is robust to pose estimation errors. The method has been tested on various datasets, including the KITTI dataset, Argoverse 2, and a survey sensor dataset, demonstrating its effectiveness in real-world scenarios. DUFOMap is a promising approach for dynamic awareness mapping in robotics.DUFOMap is a novel dynamic awareness mapping framework designed for efficient online processing. It identifies and classifies fully observed empty regions using ray casting, which helps determine dynamic points. DUFOMap outperforms state-of-the-art methods in terms of accuracy and computational efficiency across various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. The method is based on UFOMap and operates on point clouds processed in the voxel structure. It classifies points as static or dynamic by examining the void regions, which are areas that have been observed as empty. DUFOMap can be used for both offline map cleaning and online detection of dynamic points. It is open-source and has been validated across multiple datasets, sensors, and scenarios, demonstrating its generality, computational efficiency, and broad usability. The method is robust to sensor noise and localization errors, and it generalizes well across different scenarios and sensors. DUFOMap is compared with state-of-the-art methods such as Removert, ERASOR, OctoMap, and Dynablox, showing superior performance in terms of accuracy and computational efficiency. The method is particularly effective in handling dynamic and complex environments, and it is capable of removing dynamic points from point cloud maps with high accuracy. DUFOMap is also efficient in terms of execution time, outperforming other methods in both dense and sparse sensor settings. The method is capable of handling different sensors and scenarios, and it is robust to pose estimation errors. The method has been tested on various datasets, including the KITTI dataset, Argoverse 2, and a survey sensor dataset, demonstrating its effectiveness in real-world scenarios. DUFOMap is a promising approach for dynamic awareness mapping in robotics.