基于无人机点云数据的堆体体积测量方法、装置、设备及存储介质
By collecting point cloud data using drones and utilizing the PointNet++ network and Alpha Shapes algorithm, efficient and accurate heap volume measurement was achieved, solving the problems of low computational efficiency and insufficient accuracy in traditional methods, and enabling volume measurement to be completed in complex environments.
CN120976298BActive Publication Date: 2026-07-17JINLING INST OF TECH
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JINLING INST OF TECH
- Filing Date
- 2025-08-04
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Traditional volume measurement methods are inefficient and inaccurate, and cannot effectively scan the target object, especially in complex environments where they cannot reach the measurement location.
Method used
Point cloud data is collected by drones, classified using a deep learning model based on the PointNet++ network, and the volume of the target pile is calculated by combining the Alpha Shapes algorithm, thus achieving efficient and accurate volume measurement.
Benefits of technology
It improves computational efficiency and accuracy, effectively reaches the measurement location, and solves the problem that traditional methods cannot completely cover the target object.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN120976298B_ABST
Abstract
本发明公开了基于无人机点云数据的堆体体积测量方法、装置、设备及存储介质，涉及点云数据处理技术领域。方案包括：在Web端，通过基于PointNet++网络构建的预设深度学习模型对无人机采集的原始点云数据进行分类，通过Alpha Shapes算法，计算目标点云数据对应的目标堆体体积，提高了计算效率和计算精度，同时无人机执行点云扫描采集任务既可以有效地到达测量位置，又可以解决无法完整覆盖目标物的问题。
Need to check novelty before this filing date? Find Prior Art