通过自更新语义知识库，让单光子激光雷达在低信噪比下仍能识别未知目标。
Semantic Temporal Single-photon LiDAR
- 将单光子激光雷达识别任务视为语义通信，构建可自更新的语义知识库
- 在真实场景中对9类未知目标识别率达89%，无更新机制时仅为66%
- 适合动态环境中的鲁棒目标识别，无需重训练神经网络
时间单光子（TSP）激光雷达为远距离无影像目标识别提供了低成本、小体积、低功耗的解决方案。然而，现有方法在未知目标出现的开放集场景下表现不佳，且在低信噪比和短采集时间（光子数少）条件下性能显著下降。受语义通信启发，本文提出基于自更新语义知识库（SKB）的语义TSP-LiDAR，将目标识别建模为语义通信过程。仿真与实验结果表明，该方法在低信噪比和有限采集时间下均优于传统方法。更重要的是，其自更新机制可动态将新遇目标的语义特征存入SKB，实现持续适应而无需重训练神经网络。真实实验中，对9类未知目标的识别准确率达89%，未启用更新机制时仅为66%。这些发现展示了该框架在复杂动态环境中实现自适应、鲁棒目标识别的潜力。
原文摘要 · Abstract (English)
Temporal single-photon (TSP-) LiDAR presents a promising solution for imaging-free target recognition over long distances with reduced size, cost, and power consumption. However, existing TSP-LiDAR approaches are ineffective in handling open-set scenarios where unknown targets emerge, and they suffer significant performance degradation under low signal-to-noise ratio (SNR) and short acquisition times (fewer photons). Here, inspired by semantic communication, we propose a semantic TSP-LiDAR based on a self-updating semantic knowledge base (SKB), in which the target recognition processing of TSP-LiDAR is formulated as a semantic communication. The results, both simulation and experiment, demonstrate that our approach surpasses conventional methods, particularly under challenging conditions of low SNR and limited acquisition time. More importantly, our self-updating SKB mechanism can dynamically update the semantic features of newly encountered targets in the SKB, enabling continuous adaptation without the need for extensive retraining of the neural network. In fact, a recognition accuracy of 89% is achieved on nine types of unknown targets in real-world experiments, compared to 66% without the updating mechanism. These findings highlight the potential of our framework for adaptive and robust target recognition in complex and dynamic environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。