面向边缘设备点云分类的合成激光雷达数据生成与确定性下采样

中文摘要 AI 辅助

将三维深度学习框架部署到低功耗嵌入式处理器时，空间数据的非结构化特性以及神经网络推理前常用的资源密集型距离排序算法会成为瓶颈。为解决这一差距，本文提出了一种专为在树莓派5（Raspberry Pi 5）上原生执行优化的硬件受限工作流。为弥补无噪声、干净的计算机辅助设计（CAD）数据集与真实传感器数据之间的现实差距，我们采用基于物理的模拟构建了合成激光雷达（LiDAR）数据集。跨数据集评估表明，在干净CAD数据上训练的网络在合成LiDAR传感器数据上评估时，分类准确率大幅下降，凸显了感知器感知训练的迫切需求。为解决边缘中央处理器（CPU）上传统几何预处理的延迟瓶颈，我们集成了一个独立的、以特征驱动的关键点层（Critical Points Layer, CPL）作为前端滤波器。结果显示，预训练的CPL可确定性地将原始1024点云压缩为40至60个唯一坐标的子集。在ARM Cortex-A76处理器上进行性能分析时，完整流水线实现了约50帧每秒（FPS）的推理吞吐量，同时保持了88.36%的实例分类准确率，证明了边缘设备上确定性实时三维感知的可行性。

英文摘要

Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference. To address this gap, this paper presents a hardware-constrained workflow optimized for native execution on the Raspberry Pi 5. To account for the reality gap between noiseless, clean computer-aided design (CAD) datasets and real-world sensor data, we use physics-based simulation to construct a synthetic LiDAR dataset. Cross-dataset evaluations demonstrate a substantial drop in classification accuracy when networks trained on clean CAD data are evaluated on synthetic LiDAR sensor data, highlighting the critical need for sensor-aware training. To address the latency bottleneck of traditional geometric preprocessing on edge CPUs, we integrate an isolated, feature-driven Critical Points Layer (CPL) as a frontend filter. Our results show that the pretrained CPL deterministically compresses raw 1024-point clouds to a subset of 40 to 60 unique coordinates. When profiled on the ARM Cortex-A76 processor, the complete pipeline achieves an inference throughput of approximately 50 FPS while maintaining an instance classification accuracy of 88.36%, demonstrating the viability of deterministic real-time 3D perception at the edge.

发表机构

• Chemnitz University of Technology（开姆尼茨工业大学）

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