  3D single object tracking is a key issue for autonomous following robot,
where the robot should robustly track and accurately localize the target for
efficient following. In this paper, we propose a 3D tracking method called
3D-SiamRPN Network to track a single target object by using raw 3D point cloud
data. The proposed network consists of two subnetworks. The first subnetwork is
feature embedding subnetwork which is used for point cloud feature extraction
and fusion. In this subnetwork, we first use PointNet++ to extract features of
point cloud from template and search branches. Then, to fuse the information of
features in the two branches and obtain their similarity, we propose two cross
correlation modules, named Pointcloud-wise and Point-wise respectively. The
second subnetwork is region proposal network(RPN), which is used to get the
final 3D bounding box of the target object based on the fusion feature from
cross correlation modules. In this subnetwork, we utilize the regression and
classification branches of a region proposal subnetwork to obtain proposals and
scores, thus get the final 3D bounding box of the target object. Experimental
results on KITTI dataset show that our method has a competitive performance in
both Success and Precision compared to the state-of-the-art methods, and could
run in real-time at 20.8 FPS. Additionally, experimental results on H3D dataset
demonstrate that our method also has good generalization ability and could
achieve good tracking performance in a new scene without re-training.
