| Summary: | Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scenes, driverless ground vehicles or robots usually revisit the same place from a different position, meaning that the descriptor cannot give a uniform description of similar scenes, failing LCD. Against this problem, this paper proposes a 3D point cloud descriptor with Twice Projection (3PCD-TP) for the calculation of the similarities between scenes. First, we redefined the origin and primary direction of point clouds according to their distribution and unified their coordinate system, thereby reducing the interference in position recognition due to the rotation and translation of sensors. Next, using the semantic and altitudinal information of point clouds, we generated the 3D descriptor 3PCD-TP with multidimensional features to enhance its ability to describe similar scenes. Following this, we designed a weighting similarity calculation method to reduce the false detection rate of LCD by taking advantage of the property that 3PCD-TP can be projected from multiple angles. Finally, we validated our method using KITTI and the Jilin University (JLU) campus dataset. The experimental results show that our method demonstrated a high level of precision and recall and exhibited greater performance in the face of scenes with reverse loop closure, such as opposite lanes.
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