  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.
