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The 3D reconstruction of simultaneous localization and mapping (SLAM) is animportant topic in the field for transport systems such as drones, servicerobots and mobile AR/VR devices. Compared to a point cloud representation, the3D reconstruction based on meshes and voxels is particularly useful forhigh-level functions, like obstacle avoidance or interaction with the physicalenvironment. This article reviews the implementation of a visual-based 3D scenereconstruction pipeline on resource-constrained hardware platforms. Real-timeperformances, memory management and low power consumption are critical forembedded systems. A conventional SLAM pipeline from sensors to 3Dreconstruction is described, including the potential use of deep learning. Theimplementation of advanced functions with limited resources is detailed. Recentsystems propose the embedded implementation of 3D reconstruction methods withdifferent granularities. The trade-off between required accuracy and resourceconsumption for real-time localization and reconstruction is one of the openresearch questions identified and discussed in this paper.

A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems