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.

翻译：获取高分辨率、精准的沟道地形及堆积物条件是进行沟谷型泥石流研究的前置挑战。目前广泛应用的卫星遥感与无人机摄影测量等技术难以精确观测山区长深沟谷的内部状态，尤其是汶川震区内的此类沟谷。SLAM是一种新兴的三维制图技术，然而长深沟谷极端崎岖的环境即使对最先进的SLAM系统也构成两大挑战：（1）非典型特征；（2）传感器的剧烈晃动与振荡。这些问题导致SLAM结果存在较大偏差与大量噪声。为改善此类环境下的SLAM制图性能，我们提出了一套基于SLAM的先进沟道探测与制图系统，命名为AscDAMs。该系统对SLAM后处理结果进行三项主要改进：（1）数字正射影像辅助偏差校正算法大幅消除系统误差；（2）点云平滑算法显著降低噪声；（3）横截面提取算法实现了对沟道堆积物及其变化的定量评估。2023年2月与11月，我们在中国汶川县楚头沟开展了两次野外实验，分别代表雨季前后的观测结果。我们验证了AscDAMs能够显著提升SLAM制图效果，从而推动SLAM在特殊困难环境中的应用。所提方法弥补了现有技术在探测泥石流沟道内部细节（包括精细沟道形态、侵蚀模式、堆积物辨识、体积估算及变化检测）方面的不足。该方法有助于深化全尺度泥石流机理、长期震后演化及灾害评估的研究。