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.

提出了一种基于STD方法的快速、稳健的图像特征三角描述符（IFTD），旨在提高驾驶场景中地点识别的效率和准确性，通过从点云的BEV投影图像中提取关键点并将其构造成三角描述符，通过匹配这些特征三角形，实现了精确的地点识别，并计算了两个关键帧之间的4自由度位姿估计，并进一步采用图像相似性检查来进行最终的地点识别，三个公共数据集上的实验结果表明，我们的IFTD比最先进的方法具有更强的稳健性和准确性，计算开销较低。

IFTD：用于驾驶场景中循环检测的图像特征三角描述符