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arXiv · 2605.02275

EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

Pierpaolo Serio·Hetian Wang·Zixiang Wei·Vincenzo Infantino·Lorenzo Gentilini·Lorenzo Pollini·Valentina Donzella
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Abstract

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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Pierpaolo Serio, Hetian Wang, Zixiang Wei, Vincenzo Infantino, Lorenzo Gentilini, Lorenzo Pollini, Valentina Donzella. 2026-05-04. EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition. https://arxiv.org/abs/2605.02275

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