Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

Loop closure detection is a key challenge in visual simultaneous localization and mapping (SLAM) systems, which has attracted significant research interest in recent years. It entails correctly determining whether a scene has previously been visited by a mobile robot and completely establishing the consistent maps of motion. There are many loop closure detection methods that have been proposed, but most of these algorithms are handcrafted features-based and perform weak robustness to illumination variations. In this paper, we investigate a Siamese Convolutional Neural Network (SCNN) to solve the task of loop closure detection in RGB-D SLAM. Firstly, we use a pre-trained SCNN model to extract features as image descriptors; then, the L2 norm distance is adopted as a similarity metric between descriptors. In terms of the learned features for matching, there are two key issues for discussion: (1) how to define an appropriate loss as supervision (utilizing the cross-entropy loss, the contrastive loss, or the combination of two); and (2) how to combine the appearance information in RGB images and position information in depth images (utilizing early fusion, mid-level fusion or late fusion). We compare our proposed method of different baseline by experiments carried out on two public datasets (New College and NYU), and our performance outperforms the state-of-the-art.

Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.