AscDAMs：基于SLAM的先进河道探测与制图系统
AscDAMs: Advanced SLAM-based channel detection and mapping system
AI总结：
针对现有技术难以精确观测深长冲沟河道内部的问题，提出基于SLAM的AscDAMs系统，通过三项后处理改进提升制图精度，经野外实验验证可有效支撑泥石流相关研究与灾害评估。
摘要：
获取高分辨率、高精度的河道地形与堆积物状况是沟槽型泥石流研究的首要挑战。目前，卫星成像、无人机摄影测量等广泛应用的制图技术难以精确观测山区深长冲沟的河道内部状况，尤其是汶川地震区域的冲沟。SLAM是一种新兴的三维制图技术，但深长冲沟内极端崎岖的环境即使对最先进的SLAM也构成两大挑战：（1）非典型特征；（2）传感器的剧烈摇晃与振荡。这些问题导致SLAM结果偏差大、噪声多。为提升此类环境下的SLAM制图效果，我们提出了一种基于SLAM的先进河道探测与制图系统，即AscDAMs。该系统针对SLAM结果的后处理进行了三项主要改进：（1）数字正射影像辅助偏差校正算法，大幅消除系统误差；（2）点云平滑算法，显著减少噪声；（3）横断面提取算法，可对河道堆积物及其变化进行定量评估。2023年2月和11月，我们在中国汶川县船头沟开展了两次野外实验，分别对应雨季前后的观测。实验证明，AscDAMs能够大幅改善SLAM结果，推动SLAM应用于这类极具挑战性的环境制图。所提方法弥补了现有技术在泥石流河道内部探测方面的不足，涵盖精细河道形态、侵蚀模式、堆积物区分、体积估算和变化检测等方面，有助于深化全尺度泥石流机制、震后长期演化及灾害评估研究。
英文摘要：
Obtaining high-resolution, accurate channel topography and deposit conditions is the prior challenge for the study of channelized debris flow. Currently, wide-used mapping technologies including satellite imaging and drone photogrammetry struggle to precisely observe channel interior conditions of mountainous long-deep gullies, particularly those in the Wenchuan Earthquake region. SLAM is an emerging tech for 3D mapping; however, extremely rugged environment in long-deep gullies poses two major challenges even for the state-of-art SLAM: (1) Atypical features; (2) Violent swaying and oscillation of sensors. These issues result in large deviation and lots of noise for SLAM results. To improve SLAM mapping in such environments, we propose an advanced SLAM-based channel detection and mapping system, namely AscDAMs. It features three main enhancements to post-process SLAM results: (1) The digital orthophoto map aided deviation correction algorithm greatly eliminates the systematic error; (2) The point cloud smoothing algorithm substantially diminishes noises; (3) The cross section extraction algorithm enables the quantitative assessment of channel deposits and their changes. Two field experiments were conducted in Chutou Gully, Wenchuan County in China in February and November 2023, representing observations before and after the rainy season. We demonstrate the capability of AscDAMs to greatly improve SLAM results, promoting SLAM for mapping the specially challenging environment. The proposed method compensates for the insufficiencies of existing technologies in detecting debris flow channel interiors including detailed channel morphology, erosion patterns, deposit distinction, volume estimation and change detection. It serves to enhance the study of full-scale debris flow mechanisms, long-term post-seismic evolution, and hazard assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
