Forest road surface detection using LiDAR-SLAM and U-Net

Nakagomi, Hiroyuki; Fuse, Yoshihiro; Nagata, Yasuki; Hosaka, Hidehiko; Miyamoto, Hironaga; Yokozuka, Masashi; Kamimura, Akiya; Watanabe, Hiromi; Tanzawa, Tsutomu; Kotani, Shunsuke

doi:10.1109/ieeeconf49454.2021.9382683

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…UAV photogrammetry, which refers to the combination of a UAV with a Light Detection and Ranging (LiDAR) sensor, has advanced rapidly over the last decade, enabling three-dimensional modeling for the protection and management of natural ecosystems, among others [57]. LiDAR sensors have a high resolution and accuracy when measuring objects [66] and represent a promising technology for road maintenance in the near future [67]. Zeybek and Bicici [68] have shown that LiDAR sensors can detect deformations on the road surface.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the integration of LiDAR sensors in smartphones, they might be a cost-effective remote-viewing alternative in the near future [54], facilitating quick and accurate data collection [69] and, most importantly, easy mapping and inspection of forest roads lacking GNSS coverage [54,[69][70][71]. Nakagomi et al [66] introduced a novel approach for road monitoring by leveraging Light Detection and Ranging Simultaneous Localization and Mapping (LiDAR-SLAM) and a convolutional neural network (U-Net) architecture. LiDAR-SLAM demonstrates high accuracy in estimating road form in response to environmental variations, whilst the U-NET architecture proves to be useful in estimating pavement conditions.…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

Siafali,

Tsioras

2024

Forests

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The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the surface of a multi-use trail, as well as to calculate sediment deposition and generate a flood hazard map in a partially forested region. The eBee X mapping Unmanned Aerial Vehicle (UAV) equipped with the senseFly S.O.D.A. 3D camera and Real-Time Kinematic (RTK) technology flew over the study area of 149 ha in Northern Greece at an altitude of 120 m and achieved a high spatial resolution of 2.6 cm. The specific constellation of fixed-wing equipment makes the use of ground control points obsolete, compared to previous, in most cases polycopter-based, terrain deformation research. Employing the same methodology, two distinct classifications were applied, utilizing the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for analysis. The Geolocation Errors and Statistics for Bundle Block Adjustment exhibited a high level of accuracy in the model, with the mean values for each of the three directions (X, Y, Z) being 0.000023 m, −0.000044 m, and 0.000177 m, respectively. The standard deviation of the error in each direction was 0.022535 m, 0.019567 m, and 0.020261 m, respectively. In addition, the Root Mean Square (RMS) error was estimated to be 0.022535 m, 0.019567 m, and 0.020262 m, respectively. A total of 20 and 30 altitude categories were defined at a 4 cm spatial resolution, each assigned specific ranges of values, respectively. The area of each altitude category was quantified in square meters (m2), while the volume of each category was measured in cubic meters (m3). The development of a Deformation Classification Model for the deck of a trail or forest road, coupled with the computation of earthworks and the generation of a flood hazards map, represents an efficient approach that can provide valuable support to forest managers during the planning phase or maintenance activities of hiking trails and forest roads.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

Siafali,

Tsioras

2024

Forests

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Multisensors Fusion SLAM-Aided Forest Plot Mapping With Backpack Dual-LiDAR System

Yang,

Xing,

Xing

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Developing Forest Road Recognition Technology Using Deep Learning-Based Image Processing

Lee,

Kim,

et al. 2024

Forests

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This study develops forest road recognition technology using deep learning-based image processing to support the advancement of autonomous driving technology for forestry machinery. Images were collected while driving a tracked forwarder along approximately 1.2 km of forest roads. A total of 633 images were acquired, with 533 used for the training and validation sets, and the remaining 100 for the test set. The YOLOv8 segmentation technique was employed as the deep learning model, leveraging transfer learning to reduce training time and improve model performance. The evaluation demonstrates strong model performance with a precision of 0.966, a recall of 0.917, an F1 score of 0.941, and a mean average precision (mAP) of 0.963. Additionally, an image-based algorithm is developed to extract the center from the forest road areas detected by YOLOv8 segmentation. This algorithm detects the coordinates of the road edges through RGB filtering, grayscale conversion, binarization, and histogram analysis, subsequently calculating the center of the road from these coordinates. This study demonstrates the feasibility of autonomous forestry machines and emphasizes the critical need to develop forest road recognition technology that functions in diverse environments. The results can serve as important foundational data for the future development of image processing-based autonomous forestry machines.

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Forest road surface detection using LiDAR-SLAM and U-Net

Cited by 3 publications

References 17 publications

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

Multisensors Fusion SLAM-Aided Forest Plot Mapping With Backpack Dual-LiDAR System

Developing Forest Road Recognition Technology Using Deep Learning-Based Image Processing

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