  In this work, we propose a fast and robust Image Feature Triangle Descriptor
(IFTD) based on the STD method, aimed at improving the efficiency and accuracy
of place recognition in driving scenarios. We extract keypoints from BEV
projection image of point cloud and construct these keypoints into triangle
descriptors. By matching these feature triangles, we achieved precise place
recognition and calculated the 4-DOF pose estimation between two keyframes.
Furthermore, we employ image similarity inspection to perform the final place
recognition. Experimental results on three public datasets demonstrate that our
IFTD can achieve greater robustness and accuracy than state-of-the-art methods
with low computational overhead.
