Directional PointNet: 3D Environmental Classification for Wearable Robotics
This work addresses environmental classification for wearable robotics to improve motion intent prediction, but it is incremental as it modifies an existing PointNet method for efficiency.
The paper tackles the problem of classifying 3D environments for wearable robotics by proposing a directional PointNet that directly processes point clouds, achieving 99% accuracy on terrain classification tasks.
Environmental information can provide reliable prior information about human motion intent, which can aid the subject with wearable robotics to walk in complex environments. Previous researchers have utilized 1D signal and 2D images to classify environments, but they may face the problems of self-occlusion. Comparatively, 3D point cloud can be more appropriate to depict environments, thus we propose a directional PointNet to classify 3D point cloud directly. By utilizing the orientation information of the point cloud, the directional PointNet can classify daily terrains, including level ground, up stairs, and down stairs, and the classification accuracy achieves 99% for testing set. Moreover, the directional PointNet is more efficient than the previous PointNet because the T-net, which is utilized to estimate the transformation of the point cloud, is removed in this research and the length of the global feature is optimized. The experimental results demonstrate that the directional PointNet can classify the environments robustly and efficiently.