Abstract
Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic–visual–geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference.
Highlights
Loop closure detection is an important module in visual Simultaneous localization and mapping (SLAM) [3]
Inspired by the above modules, a loop closure detection algorithm based on a semantic bag of words and a semantic landmark vector is proposed in this paper
A semantic loop closure detection (SLCD) approach was designed to reduce the issue of semantic inconsistency in [48]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Traditional loop detection algorithms rely on visual appearance These methods extract various features [5,6,7] to compare similarities of images. 2021, 13, 3520 and Rotated BRIEF (ORB) feature were employed to complete the loop closure detection in ORB-SLAM [11,12,13] Most of these methods utilize the bag-of-words model [14], which generates words from feature points to build a dictionary structure and queries the similarity of words in each image to make a loop judgment. Inspired by the above modules, a loop closure detection algorithm based on a semantic bag of words and a semantic landmark vector is proposed in this paper. A semantic landmark vector was designed that can express semantic and geometric information of images and improve the robustness of loop closure detection.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have