This algorithm is designed to measure the dimensions (length, width and height) of cuboids within a pointcloud generated by an iPad LiDAR. The algorithm expects a pointcloud .ply file as and the x,y,z coordinates of a point in the top plane of the box as input parameters. It then processes the pointcloud and returns the measurement of the length, height, and width of the cuboid. In addition, it also provides a visual representation of the detected cuboid with a bounding box around it. This makes it easy for users to visually verify the measurement.
- python 3.x
- open3d
- numpy
- pyransac
The algorithm is implemented in a Jupyter notebook (.ipynb file). To run the code, make sure you have the above dependencies installed and open the notebook in Jupyter.
The algorithm is designed to measure cuboids in a pointcloud capture from an iPad LiDAR. It takes a pointcloud .ply capture and the x,y,z coordinates of a point in the top plane of the box as an input parameter, processes the image, and returns the measurement of the length, height, and width. Note that the height will always be measured between the top and bottom plane(floor) of the box. In addition, it also provides a visual representation of the detected cuboid with a bounding box around it. This makes it easy for users to visually verify the measurement.
The algorithm is based on the assumption that you know the coordinates of a point in the top plane of the cuboid. This point can be found with the select_point_in_top_plane tool Additionally, the accuracy en efficiency of the algorithm depends on the density and quality of the pointcloud data.
I would like to express my gratitude to Gilles Vandewiele for reviewing my research and for his valuable feedback and recommendations. I would also like to thank Vic Degreave for his help with the math and logic of the algorithm. His contribution was invaluable to the development and improvement of the algorithm.
- Open3D library documentation: http://www.open3d.org/docs/index.html
- Pyransac library documentation: https://pyransac.readthedocs.io/en/latest/
- Numpy library documentation: https://numpy.org/doc/