在基于事件的边缘部署 SLAM 中,用于节能运动估计的粗微对比度最大化
Kyeongpil Min1, Jongin Choi1, Woojoo Lee1
1Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.
Micromachines
|February 27, 2026
概括
我们开发了粗微对比最大化 (CCMAX) 技术,以高效地基于事件的运动估计. 在同时定位和映射 (SLAM) 系统中,CCMAX显著降低了计算和能源消耗,实现了与传统方法相比的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 基于事件的视觉传感器提供高时间分辨率和低功耗,非常适合边缘机器人和SLAM.
- 对比度最大化 (CMAX) 是使用事件数据进行自我运动估计的直接几何方法.
- 传统的CMAX是计算密集的,因为它需要反复处理完整的事件集和高分辨率图像.
研究的目的:
- 开发一个计算效率高的CMAX变体,用于基于事件的运动估计.
- 为了降低CMAX的计算和能源成本,而不会牺牲准确性.
- 在资源有限的边缘平台上实现实时SLAM.
主要方法:
- 拟议的粗微对比最大化 (CCMAX),是一种计算意识的CMAX方法.
- 在早期阶段实施了逐步增加IWE分辨率和粗网事件亚抽样.
- 在最后的优化阶段保留了全分辨率精细化.
主要成果:
- 在标准基准上,CCMAX的准确性与全分辨率CMAX相美.
- 浮点交易 (FLOP) 减少了高达42%.
- 在RISC-V边缘SoC上,已证明能耗降低87%
结论:
- 在基于事件的运动估计中,CCMAX可显著降低计算成本和能源消耗.
- 拟议的方法适用于实时边缘SLAM在电力和资源受限制的平台上.
- CCMAX为先进的机器人应用提供了一个节能前端.
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