⚡ 本页包含 AI 生成的分析内容，仅供参考
本文提出一款用于微飞行器自主导航的立体深度处理器，实现了1920×1080分辨率30fps的实时处理。通过硬件加速半全局匹配算法，解决了传统CPU运行速度慢、功耗高的问题，能效达到2.3TOPS/W。
realizing autonomous navigation on micro-aerial vehicles (MAVs). The state-of-the-art semi-global matching (SGM) algorithm has become favored for its high accuracy. In particular, it effectively handles low texture regions due to its global optimization of the disparity between a left and right image over the entire frame. However, SGM involves massively parallel computation (~2TOP/s) and extremely high bandwidth memory access (38.6Tb/s) for 30fps HD resolution. This leads to ~20s runtime for an HD image pair on a 3GHz CPU [1] requiring ~386MB memory and >35W power consumption. Together, these factors place it well outside the realm of MAVs. Prior ASIC implementations have used either simpler local methods [2] or aggressively truncated global algorithms [3] that produce a depth map with
Ziyun Li, Qing Dong, Mehdi Saligane, Benjamin Kempke, Shijia Yang,
Zhengya Zhang, Ronald Dreslinski, Dennis Sylvester, David Blaauw, Hun Seok Kim University of Michigan, Ann Arbor, MI Precise depth estimation is a key kernel function to