Yu Mingfei, Zhang Lei, Wang Wufan, Huang Hua
IEEE Trans Image Process. 2021;30:8873-8885. doi: 10.1109/TIP.2021.3116898. Epub 2021 Oct 28.
Loop closure detection plays an important role in many Simultaneous Localization and Mapping (SLAM) systems, while the main challenge lies in the photometric and viewpoint variance. This paper presents a novel loop closure detection algorithm that is more robust to the variance by using both global and local features. Specifically, the global feature with the consolidation of photometric and viewpoint invariance is learned by a Siamese Network from the intensity, depth, gradient and normal vectors distribution. The local feature with rotation invariance is based on the histogram of relative pixel intensity and geometric information like curvature and coplanarity. Then, these two types of features are jointly leveraged for the robust detection of loop closures. The extensive experiments have been conducted on the publicly available RGB-D benchmark datasets like TUM and KITTI. The results demonstrate that our algorithm can effectively address challenging scenarios with large photometric and viewpoint variance, which outperforms other state-of-the-art methods.
闭环检测在许多同时定位与地图构建（SLAM）系统中起着重要作用，而主要挑战在于光度和视角变化。本文提出了一种新颖的闭环检测算法，该算法通过同时使用全局和局部特征，对这些变化具有更强的鲁棒性。具体而言，通过暹罗网络从强度、深度、梯度和法向量分布中学习整合了光度和视角不变性的全局特征。具有旋转不变性的局部特征基于相对像素强度直方图以及诸如曲率和共面性等几何信息。然后，联合利用这两种类型的特征进行鲁棒的闭环检测。我们在诸如TUM和KITTI等公开可用的RGB-D基准数据集上进行了广泛的实验。结果表明，我们的算法能够有效应对具有大光度和视角变化的挑战性场景，性能优于其他现有先进方法。
