提出轻量级激光雷达定位模型，平衡精度与效率
EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition
- 用鸟瞰图+图像网络实现轻量化激光雷达定位
- FP16精度下性能接近全精度，功耗更低
- 揭示量化策略对不同架构的差异影响，适合边缘部署研究
地点识别对长期自主导航至关重要，可实现回环检测与一致建图。尽管深度学习提升了性能，但在资源受限平台部署仍具挑战。本文通过鸟瞰图表示，使轻量级图像网络适用于边缘AI场景下的激光雷达定位。采用统一的全局池化与线性投影描述符方案，评估了无聚合头的代表性网络在FP32、FP16和INT8量化下的表现。实验揭示精度、鲁棒性与效率间的权衡：FP16在成本更低的前提下匹配FP32性能，而INT8则引发依赖架构的性能下降。整体结果为面向具体应用场景的神经网络量化部署提供了坚实基础。
原文摘要 · Abstract (English)
Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved performance, deploying such models on resource-constrained platforms remains challenging. This work explores efficient LiDAR-based place recognition for EdgeAI by leveraging Bird's Eye View representations to enable lightweight image-based networks. We benchmark representative architectures without aggregation heads using a unified descriptor scheme based on global pooling and linear projection, and evaluate performance under FP32, FP16, and INT8 quantization. Experiments reveal trade-offs between accuracy, robustness, and efficiency: FP16 matches FP32 with lower cost, while INT8 introduces architecture-dependent degradation. Overall, the presented results are a strong basis for future research on 'use-case'-aware quantisation of Neural Networks for Edge deployment.
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