Loop Closure Detection by Using Global and Local Features With Photometric and Viewpoint Invariance

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.