A method for measuring the volume of a stockpile based on slam

By using a SLAM-based method, solid-state lidar and inertial odometry are used to acquire point clouds of stockpiles, and point cloud preprocessing and Delaunay triangulation are performed. This solves the problems of low accuracy and efficiency in stockpile volume measurement, and realizes high-precision and simple volume measurement of irregularly shaped stockpiles.

CN118066997BActive Publication Date: 2026-07-24CHINA UNIV OF MINING & TECH
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Patent Information

Application Number
CN202410045027.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-01-11
Publication Date
2026-07-24
Estimated Expiration
2044-01-11

AI Technical Summary

Technical Problem

Existing technologies for measuring stockpile volume suffer from low accuracy and efficiency, complex operation, and large errors, especially for irregularly shaped stockpiles, making it difficult to meet the needs of frontline workers.

Method used

A SLAM-based approach was adopted, using solid-state lidar and inertial odometry to acquire point clouds of the stockpile. The FastLIO2 algorithm was used for point cloud ROI extraction and preprocessing. Combined with RANSAC ground segmentation, point cloud filtering and Delaunay triangulation, the three-dimensional volume of the stockpile was calculated.

Benefits of technology

It achieves high-precision and high-speed material volume measurement, is suitable for irregular shapes, is easy to operate, has small error and good consistency, and is especially suitable for measuring the volume of irregularly shaped material piles.

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Abstract

The application discloses a kind of based on SLAM's stockpile volume measurement method, by using solid-state laser radar scanning to obtain stockpile surface point cloud, by inertial odometry in solid-state laser radar obtains point cloud data when synchronously obtaining the pose and mileage data of measuring equipment, then in data processor, using the point cloud ROI extraction method based on SLAM-Odom information obtains the rectangular point cloud region containing stockpile three-dimensional model, and by downsampling, segmentation, filtering, clustering, transformation, smoothing, point cloud is preprocessed, then using projection method obtains bottom surface point cloud, and using Delaunay triangulation to the target point cloud is meshed, the approximate three-prism volume obtained by each section is calculated and accumulated to obtain the final volume.The application can be operated by front-line practitioners under the premise that measurement accuracy and measurement efficiency are both good, and the error is minimal when different personnel measure, with good consistency.
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