An Efficient 3-D Point Cloud Place Recognition Approach Based on Feature Point Extraction and Transformer

In dynamic environments, sensor occlusions and viewpoint changes occur frequently, leading to challenges for point-based place recognition retrieval. Existing deep learning-based methods are impossible to possess the strengths of high detection accuracy, small network model, and rapid detection simultaneously, making them inapplicable to real-life situations. In this article, we propose an efficient 3-D point cloud place recognition approach based on feature point extraction and transformer (FPET-Net) to improve the detection effect of place recognition and reduce the model computation. We first introduce a feature point extraction module, which can greatly reduce the size of the point cloud and preserve the data features, further reducing the impact of environmental changes on data acquisition. Then, a point transformer module is developed to control the computational effort while extracting the global descriptors by discriminative properties. Finally, a feature similarity network module computes the global descriptor similarity using a bilinear tensor layer with lower parameters correlated across latitudes. Experiments show that the parameters of our algorithm are 2.7 times smaller than the previous lightest efficient 3D point cloud feature learning for large-scale place recognition (EPC-Net), and the computation speed of one frame point cloud is 4.3 times faster. The network also achieves excellent results with a maximum F1 score of 0.975 in place recognition experiments based on the KITTI dataset.