We propose a directional PointNet to directly classify the 3D point cloud. First, an inertial measurement unit (IMU) is used to offset the orientation of point cloud. Then the directional PointNet can accurately classify the daily commuted terrains, including level ground, climbing up stairways, and walking down stairs. A classification accuracy of 98% has been achieved in tests. Moreover, the directional PointNet is more efficient than the previously used PointNet because the T-net, which is utilized to estimate the transformation of the point cloud, is not used in the present approach, and the length of the global feature is optimized. The experimental results demonstrate that the directional PointNet can classify the environments in robust and efficient manner.

For more related works and codes, please view my homepage: https://kuangenzhang.github.io/

Further information please contact Kuangen Zhang ([email protected]).

If you find our work useful in your research, please consider citing:

@article{zhang_directional_2019,
	title = {Directional pointnet: 3D environmental classification for wearable robotics},
	shorttitle = {Directional {PointNet}},
	urldate = {2019-03-22},
	journal = {arXiv:1903.06846 [cs]},
	author = {Zhang, Kuangen and Wang, Jing and Fu, Chenglong},
	month = mar,
	year = {2019}
}

Directional pointnet is the simplified version of pointnet. We have evaluated our network on classifying three kinds of terrains: level ground, up stairs, and down stairs.

• Classification accuracy: 98%.

• TensorFlow
• h5py

sudo apt-get install libhdf5-dev
sudo pip install h5py

• We upload our pretrained model and data, you can evaluate the performance of our network directly by running the evaluation script:

python evaluate.py

• Run the training script:

python train.py

MIT License

We acknowledge that we borrow the code from PointNet heavily.

• C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3d Classification and Segmentation,” in2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp.77–85, read.