LoCS-Net:定位卷积尖端神经网络,用于快速视觉位置识别
Ugur Akcal1,2,3, Ivan Georgiev Raikov4, Ekaterina Dmitrievna Gribkova3,5
1The Grainger College of Engineering, Department of Aerospace Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States.
Frontiers in neurorobotics
|February 13, 2025
概括
本研究介绍了用于视觉位置识别 (VPR) 的高效尖端神经网络 (SNN),显著提高了机器人应用的性能并降低了计算成本. 新的培训方法使SNN能够在具有挑战性的数据集上超过当前最先进的方法.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 神经形态工程的神经形态工程
- 计算机视觉 计算机视觉
背景情况:
- 视觉位置识别 (VPR) 对机器人导航至关重要,但面临着像感知别名和动态场景这样的挑战.
- 目前基于人工神经网络 (ANN) 的VPR方法在计算方面效率低下.
- 尖端神经网络 (SNN) 提供了潜在的计算效率,但面临训练和实时性能问题.
研究的目的:
- 为VPR.开发一个高效和可处理的端到端卷积性SNN模型.
- 提高SNN对VPR任务的培训和推断性能.
- 在神经形态硬件上展示SNN对VPR的现实世界部署能力.
主要方法:
- 为VPR开发了一个端到端的卷积性SNN模型,用于训练使用反向传播.
- 在训练期间使用漏洞的整合和发射 (LIF) 神经元的基于速率的近似值,转换为用于推断的激增LIF神经元.
- 在神经形态硬件 (英特尔卡波霍湾) 上部署芯片上使用ANN-to-SNN转换策略.
主要成果:
- 与SOTA SNNs相比,在Nordland (78.6%精度在100%回忆) 和Oxford RobotCar (45.7%) 数据集上取得了更好的性能.
- 在培训和推断时间方面取得了显著的改进.
- 在神经形态硬件上芯片上的性能显示出持续优于SNN对应器的性能,具有显著的能源效率.
结论:
- 拟议的SNN模型为VPR提供了更简单的培训管道和更高的性能.
- 该方法促进了基于SNN的VPR解决方案的快速原型设计和现实世界的部署.
- 这项工作代表了SNN基础机器人解决方案的重大进展.
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