针对深隧道内无人机定位难题，提出增强型光流测距方法。
Distance Measurement for UAVs in Deep Hazardous Tunnels
- 基于光流技术并引入预测机制，提升隧道内测距精度
- 在弱光与无纹理环境下，测距误差显著降低
- 适用于新加坡深隧污水系统等危险环境巡检
由于深隧道不可达且环境危险，无人机在其中的定位极具挑战。传统室外定位（如GPS）和室内定位（如基于WiFi、红外、超宽带等）在深隧道中均失效。为支持新加坡深隧污水系统（DTSS）的缺陷巡检，我们开发了一套基于光学流的无人机测距模块。然而，在光照不足且缺乏特征的隧道环境中，标准光流算法表现不佳。为此，我们提出了融合预测机制的增强型光流算法，显著提升了无人机在深危险隧道中的距离测量性能。
原文摘要 · Abstract (English)
The localization of Unmanned aerial vehicles (UAVs) in deep tunnels is extremely challenging due to their inaccessibility and hazardous environment. Conventional outdoor localization techniques (such as using GPS) and indoor localization techniques (such as those based on WiFi, Infrared (IR), Ultra-Wideband, etc.) do not work in deep tunnels. We are developing a UAV-based system for the inspection of defects in the Deep Tunnel Sewerage System (DTSS) in Singapore. To enable the UAV localization in the DTSS, we have developed a distance measurement module based on the optical flow technique. However, the standard optical flow technique does not work well in tunnels with poor lighting and a lack of features. Thus, we have developed an enhanced optical flow algorithm with prediction, to improve the distance measurement for UAVs in deep hazardous tunnels.
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