Image Sequence Matching Using Both Holistic and Local Features for Loop Closure Detection
Image Sequence Matching Using Both Holistic and Local Features for Loop Closure Detection
复制标题
使用整体和局部特征进行环路闭合检测的图像序列匹配
DOI：
10.1109/access.2017.2725387
复制
发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Angel Sotelo, Miguel
中科院分区：
文献类型：
--
作者：
Li, Yicheng;Hu, Zhaozheng;Angel Sotelo, Miguel
同步定位与地图绘制(SLAM)在移动机器人、智能车辆定位、智能交通系统等领域有着广泛的应用。然而，对SLAM来说，环路闭合检测是一项具有挑战性的任务。这项任务涉及识别已绘制地图的区域的困难。为此，本文提出了一种仅使用低成本单目摄像机的图像序列匹配(ISM)环路检测方法。这种方法首先将已映射的区域划分为一些“特征区域”。通过一种新的拓扑检测模型选择一个特征区域。然后，采用两种不同的特征空间对查询图像和特征区域进行序列匹配。最后，我们提出了一种新的聚类方法--投票K近邻算法来融合候选数据。结果，通过收集不同路线、覆盖不同时间和天气条件的收集数据集和公共数据集，对ISM方法进行了验证。这些路线的总长度超过10公里。实验结果表明，ISM方法能够适应不同的时间段，在不同的场景下具有良好的检测稳定性。在这些场景下，检测误差的平均值均小于1帧，检测准确率均在90%以上。与其他方法相比，该方法具有较高的精度和较强的鲁棒性。
Simultaneous localization and mapping (SLAM) has a wide range of applications, such as mobile robots, intelligent vehicle localization, and intelligent transportation system. However, loop closure detection is a challenge task for SLAM. This task concerns the difficulty of recognizing already mapped areas. To this end, this paper proposes a novel loop closure detection method called image sequence matching (ISM), which only uses a low-cost monocular camera. This method first divides the already mapped areas into some "feature-zones.'' One feature-zone is selected by a novel topological detection model. Then, we adopt two different feature spaces to make sequence matching between query image and feature-zone. Last but not least, we propose a novel clustering method called voting K-nearest neighbor to fuse candidates. As a result, the ISM method has been validated by using collection data sets and public data sets, which were collected along different routes, covering different times and weather conditions. The total lengths of these routes are more than 10 km. Experimental results show that the ISM method can adapt to different times with good detection stability in varying scenarios. The mean of detection errors is all less than 1 frame and the detection accuracies are all more than 90% in these scenarios. Compared with other methods, the proposed method has high accuracy and great robustness.