  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.
