The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper.

翻译：同时定位与地图构建（SLAM）的三维重建是无人机、服务机器人及移动AR/VR设备等运输系统领域的重要研究方向。与点云表示相比，基于网格和体素的三维重建对避障或物理环境交互等高层功能尤为有用。本文综述了在资源受限硬件平台上实现基于视觉的三维场景重建流程的研究进展。实时性能、内存管理和低功耗对嵌入式系统至关重要。本文描述了从传感器到三维重建的传统SLAM流程，包括深度学习的潜在应用，并详细阐述了在有限资源下实现高级功能的方法。近年来，已有研究提出不同粒度的三维重建方法在嵌入式环境中的实现方案。如何在实时定位与重建中权衡所需精度与资源消耗，是本文识别并讨论的未解决研究问题之一。