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π-BA: Bundle Adjustment Hardware Accelerator based on Distribution of 3D-Point Observations
IEEE Transactions on Computers ( IF 3.7 ) Pub Date : 2020-01-01 , DOI: 10.1109/tc.2020.2984611
Qiang Liu , Shuzhen Qin , Bo Yu , Jie Tang , Shaoshan Liu

Bundle adjustment (BA) is a fundamental optimization technique used in many crucial applications, including 3D scene reconstruction, robotic localization, camera calibration, autonomous driving, street view map generation, and even space exploration etc. Essentially, BA is a joint non-linear optimization problem, and one which can consume a significant amount of time and power, especially for large optimization problems. Previous approaches of optimizing BA performance heavily rely on parallel processing or distributed computing, which trade higher power consumption for higher performance. In this article we propose $\pi$π-BA, the first hardware-software co-designed BA hardware accelerator that exploits custom hardware to simultaneously achieve higher performance and power efficiency. Specifically, based on our key observation that not all 3D points appear on all images in a BA problem, we designed a Co-Observation Optimization technique to accelerate BA operations with optimized usage of memory and computation resources. In addition, we developed a hardware-friendly differentiation method, which combines the analytic and forward automatic differentiation to calculate derivatives of projection function in the BA problem. We have implemented the proposed design on an embedded FPGA SoC, and experimental results confirm that $\pi$π-BA outperforms the existing software implementations in terms of performance and power consumption.

中文翻译:

π-BA:基于 3D 点观测分布的捆绑调整硬件加速器

Bundle Adjustment (BA) 是许多关键应用中使用的基本优化技术,包括 3D 场景重建、机器人定位、相机校准、自动驾驶、街景地图生成,甚至空间探索等。 从本质上讲,BA 是一种联合非线性优化问题,以及一个会消耗大量时间和能量的问题,尤其是对于大型优化问题。以前优化 BA 性能的方法在很大程度上依赖于并行处理或分布式计算,这会以更高的功耗换取更高的性能。在本文中,我们建议$\pi$π-BA,第一个软硬件协同设计的 BA 硬件加速器,它利用自定义硬件同时实现更高的性能和能效。具体来说,基于我们在 BA 问题中并非所有 3D 点都出现在所有图像上的关键观察,我们设计了一种协同观察优化技术,以通过优化内存和计算资源的使用来加速 BA 操作。此外,我们开发了一种硬件友好的微分方法,它结合了解析和前向自动微分来计算 BA 问题中投影函数的导数。我们已经在嵌入式 FPGA SoC 上实现了所提出的设计,实验结果证实$\pi$π-BA 在性能和功耗方面优于现有的软件实现。
更新日期:2020-01-01
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