嵌入式系统中基于SLAM方法的实时3D场景重建综述
A survey on real-time 3D scene reconstruction with SLAM methods in embedded systems
AI总结：
本文综述了在资源受限嵌入式硬件上实现基于视觉的SLAM 3D场景重建流程，探讨了网格与体素表示、深度学习应用及资源限制下的高级功能实现，并指出精度与资源消耗的权衡是当前开放的研究问题。
摘要：
同步定位与建图（SLAM）的3D重建是无人机、服务机器人和移动AR/VR设备等交通系统领域的重要课题。与点云表示相比，基于网格和体素的3D重建对于避障或与物理环境交互等高级功能特别有用。本文回顾了在资源受限的硬件平台上实现基于视觉的3D场景重建流程。实时性能、内存管理和低功耗对于嵌入式系统至关重要。描述了从传感器到3D重建的传统SLAM流程，包括深度学习的潜在应用。详细介绍了在资源有限的情况下实现高级功能的方法。最近的系统提出了具有不同粒度的3D重建方法的嵌入式实现。实时定位与重建所需的精度和资源消耗之间的权衡是本文识别并讨论的开放性研究问题之一。
英文摘要：
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.
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