在越野环境中检测LiDAR扫描的可穿越区域的知识蒸
1School of Computing, Gachon University, Seongnam-si 1332, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
我们开发了一种知识蒸 (KD) 方法,使用LiDAR范围图像进行高效的越野环境细分. 这种方法平衡了准确性 (mIoU) 和计算成本 (GFLOPS) 以实现更安全的自动驾驶.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 越野自主系统需要由于不规则的地形和硬件风险而需要计算效率高的感知.
- 处理详细的3D LiDAR点云需要大量的计算资源.
- 现有的方法可能无法充分平衡实时越野导航的性能和效率.
研究的目的:
- 提出一种知识蒸 (KD) 方法来细分越野环境范围图像.
- 为了实现计算效率高的越野自动驾驶系统.
- 为了在细分精度和计算成本之间实现有利的权衡.
主要方法:
- 利用LiDAR点云范围图像,通过投影将3D数据转换为2D图像,提供深度信息.
- 采用基于软标签的知识蒸 (SLKD) 技术,将知识从大型教师网络转移到轻量级学生网络.
- 在RELLIS-3D越野数据集上评估了SLKD方法,测量了工会 (mIoU) 和GFLOPS的平均交叉点.
主要成果:
- 该SLKD方法证明了有效的知识从教师到学生网络的转移.
- 在细分精度 (mIoU) 和计算效率 (GFLOPS) 之间取得了有利的平衡.
- 使用SLKD培训的轻量级学生网络提供了较低的计算要求.
结论:
- 拟议的SLKD方法为越野环境细分提供了有效的解决方案.
- 这种方法在开发具有成本效益和效率的越野自动驾驶系统方面显示出显著的前景.
- 通过SLKD降低计算成本,可以提高在具有挑战性的地形上部署自主系统的可行性.
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