This paper presents a hybrid 3D-like grid-based mapping approach, that we called HMAP, used as a reliable and efficient 3D representation of the environment surrounding a mobile robot. Considering 3D point-clouds as input data, the proposed mapping approach addresses the representation of height-voxel (HVoxel) elements inside the HMAP, where free and occupied space is modeled through HVoxels, resulting in a reliable method for 3D representation. The proposed method corrects some of the problems inherent to the representation of complex environments based on 2D and 2.5D representations, while keeping an updated grid representation. Additionally, we also propose a complete pipeline for SLAM based on HMAPs. Indoor and outdoor experiments were carried out to validate the proposed representation using data from a Microsoft Kinect One (indoor) and a Velodyne VLP-16 LiDAR (outdoor). The obtained results show that HMAPs can provide a more detailed view of complex elements in a scene when compared to a classic 2.5D representation. Moreover, validation of the proposed SLAM approach was carried out in an outdoor dataset with promising results, which lay a foundation for further research in the topic.
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