Estimation of fill factor for earth-moving machines based on 3D point clouds

Lu, Jinxiong; Bi, Qiushi; Li, Yingnan; Li, Xuefei

doi:10.1016/j.measurement.2020.108114

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…In contrast, Guevara et al (2020) also did not consider environmental variation but showed a corresponding increase in accuracy to 95%. Lu et al (2020) considered light variation in normal environments and achieved an accuracy of 94.72%. Lu et al (2021) achieved a 95.23% accuracy in volume estimation in six normal environments.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

“…A single normal environment means that the differences between various normal environments are not considered. Lu et al (2020) performed a 3D point cloud reconstruction of a bucket using a binocular camera and combined this information with the structural information of the loader to estimate the fill factor of the loader bucket; additionally, lighting in a normal environment was considered, and a fill factor estimation accuracy of 94.72% was obtained. Guevara et al (2020) used a combination of 3D point cloud and machine learning to estimate the volume of material inside the bucket.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

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Section: Comparison Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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“…Guevara et al [9] used a binocular stereo camera to construct a 3D point cloud on the material surface of a bulldozer bucket, and used the Alpha-shape algorithm of Delaunay triangulation to estimate the effective shovel load of the bucket. Lu et al [10,11] developed a new perception system based on the stereo vision perception method, as well as advanced technologies such as point cloud registration, splicing, and surface interpolation, to realize the 3D point cloud reconstruction of materials in the loader bucket and accurate estimation of shovel loading. The aforementioned study produced an accurate assessment of the volume of materials in a single bucket during earthwork, offering reliable assurance for realtime estimation of earthwork volume and real-time evaluation of operational efficiency.…”

Section: Volume Estimationmentioning

confidence: 99%

“…(𝑙) , 𝒐 1 (𝑟) , ⋯ , 𝒐 𝑁 (𝑟) , 𝑺) ∝ 𝑃(𝑑 𝑛 |𝑺, 𝒐 𝑛 (𝑙) ) * 𝑃(𝒐 1 (𝑟) , ⋯ , 𝒐 𝑁 (𝑟) |𝒐 𝑛 (𝑙) , 𝑑 𝑛 ) (9) Assume that the prior probability is proportional to the Gaussian distribution, as shown in Eq. (10).…”

Section: 𝑃(𝑑 𝑛 |𝒐 𝑛mentioning

confidence: 99%

“…However, it is challenging to assess the operational efficiency in real time, necessitating a single shovel measurement. Existing research on single shovel measurements mainly focuses on constructing a 3D point cloud model of the bucket and the materials in the bucket, estimating the volume of materials in the bucket, and evaluating the bucket filling rate and shovel loading efficiency [8][9][10][11]. However, buckets of different earth-moving machines have different specifications and sizes.…”

Section: Introductionmentioning

confidence: 99%

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Fast estimation of loader's shovel load volume by 3D reconstruction of material piles

Wang

Lin

et al. 2023

Preprint

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Fast and accurate measurement of the volume of earthmoving materials is of great significance for the real-time evaluation of loader operation efficiency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fill factor estimation, and it has significant theoretical research and engineering application value.

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