AscDAMs: Advanced SLAM-based channel detection and mapping system

• URL: http://arxiv.org/abs/2401.13877v1
• Date: Thu, 25 Jan 2024 01:22:29 GMT
• Title: AscDAMs: Advanced SLAM-based channel detection and mapping system
• Authors: Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, Ping Shen
• Abstract summary: We propose an advanced SLAM-based channel detection and mapping system, namely AscDAMs. It features three main enhancements to post-process SLAM results. Two field experiments were conducted in Chutou Gully, Wenchuan County in China in February and November 2023.
• Score: 10.736648466109045
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration [9.727021664098485]
  This paper introduces a novel, tightly-coupled Acoustic-Visual-Inertial SLAM approach. It fuses a Doppler Velocity Log (DVL), a stereo camera, and an Inertial Measurement Unit (IMU) within a graph optimization framework. We show that our method surpasses current state-of-the-art underwater and visual-inertial SLAM systems in terms of localization accuracy and robustness.
  arXiv  Detail & Related papers  (2025-03-14T14:03:33Z)
• Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection [20.651257973799527]
  Recalibrating Attention of Industrial Anomaly Detection (RAAD) is a framework that systematically decomposes and recalibrates attention maps. HQS dynamically adjusts bit-widths based on the hierarchical nature of attention maps. We validate the effectiveness of RAAD on 32 datasets using a single 3090ti.
  arXiv  Detail & Related papers  (2024-12-11T08:31:47Z)
• UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation [12.24506241611653]
  Uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed. UDHF2-Net is a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) Mask-and-geo-knowledge-based uncertainty diffusion module (MUDM) is a self-supervised learning strategy. A frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection.
  arXiv  Detail & Related papers  (2024-06-23T15:03:35Z)
• Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian [49.21866794516328]
  3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
  arXiv  Detail & Related papers  (2024-05-30T03:18:30Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images [3.9921541182631253]
  This work proposes two approaches for identifying flooded regions in UAV aerial images. The first approach utilizes texture-based unsupervised segmentation to detect flooded areas. The second uses an artificial neural network on the texture features to classify images as flooded and non-flooded.
  arXiv  Detail & Related papers  (2022-10-04T13:11:44Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data [7.503635457124339]
  We adapt a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. We focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.
  arXiv  Detail & Related papers  (2020-05-07T03:14:00Z)
• CNN-based InSAR Coherence Classification [3.562355298993529]
  We introduce Convolutional Neural Networks (CNNs) to improve coherence-based demarcation and reduce misclassifications in completely incoherent regions. We show their effectiveness in improving coherence-based demarcation and reducing misclassifications in completely incoherent regions through intelligent preprocessing of training data.
  arXiv  Detail & Related papers  (2020-01-20T03:25:38Z)
• InSAR Phase Denoising: A Review of Current Technologies and Future Directions [9.475024122649288]
  Interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. Phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories.
  arXiv  Detail & Related papers  (2020-01-03T09:36:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.