Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.

RGB-D SLAM (Simultaneous Localization and Mapping) generally performs smoothly in a static environment. However, in dynamic scenes, dynamic features often cause wrong data associations, which degrade accuracy and robustness. To address this problem, in this paper, a new RGB-D dynamic SLAM method, PLD-SLAM, which is based on point and line features for dynamic scenes, is proposed. First, to avoid under-over segmentation caused by deep learning, PLD-SLAM combines deep learning for semantic information segmentation with the K-Means clustering algorithm considering depth information to detect the underlying dynamic features. Next, two consistency check strategies are utilized to check and filter out the dynamic features more reasonably. Then, to obtain a better practical performance, point and line features are utilized to calculate camera pose in the dynamic SLAM, which is also different from most published dynamic SLAM algorithms based merely on point features. The optimization model with point and line features is constructed and utilized to calculate the camera pose with higher accuracy. Third, enough experiments on the public TUM RGB-D dataset and the real-world scenes are conducted to verify the location accuracy and performance of PLD-SLAM. We compare our experimental results with several state-of-the-art dynamic SLAM methods in terms of average localization errors and the visual difference between the estimation trajectories and the ground-truth trajectories. Through the comprehensive comparisons with these dynamic SLAM schemes, it can be fully demonstrated that PLD-SLAM can achieve comparable or better performances in dynamic scenes. Moreover, the feasibility of camera pose estimation based on both point features and line features has been proven by the corresponding experiments from a comparison with our proposed PLD-SLAM only based on point features.

In the study of RGB-D SLAM (Simultaneous Localization and Mapping), two types of primary visual features, point and line features, have been widely utilized to calculate the camera pose. As an RGB-D camera can capture RGB and depth information simultaneously, most RGB-D SLAM methods only utilize the 2D information within the point and line features. To obtain a higher accuracy camera pose and utilize the 2D and 3D information within points and lines better, a novel geometric constraint model of points and lines (PL-GM) using an RGB-D camera is proposed in this paper. Our contributions are threefold. Firstly, the 3D points and lines generated by an RGB-D camera combining with 2D point and line features are utilized to establish the PL-GM, which is different from most models of point-line SLAM (PL-SLAM). Secondly, in addition to the 2D re-projection error of point and line features, the constraint errors of 3D points and lines are constructed and minimized likewise, and then a unified optimization model based on PL-GM is extended to the bundle adjustment model (BA). Finally, extensive experiments have been performed on two public benchmark RGB-D datasets and a real scenario sequence. These experimental results demonstrate that our method achieves a comparable or better performance than the state-of-the-art SLAM methods based on point and line features, and point features.

With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

The visual-inertial SLAM, which integrates data from monocular or stereo cameras, has garnered significant attention and development. The RGB-D camera, capable of capturing both color and depth images simultaneously, can perceive a comprehensive view of the surroundings. To fully leverage two types of measurement information from the RGB-D camera and IMU sensor for accurate pose estimation, we propose a new visual-inertial SLAM algorithm, VID-SLAM, that effectively couples the RGB-D camera with the IMU. In our proposal, we first develop an adaptive point feature detection approach that rapidly detects and tracks sufficient point features. This approach utilizes adaptive non-maximum suppression and the Kd-Tree algorithm to ensure a homogeneous distribution of point features. Secondly, we incorporate line features into the pose estimation module of the SLAM algorithm. By screening line features based on the geometric properties of vanishing points, we ensure that the detected lines align with the edges of scene objects as early as possible. Beyond the 2D re-projection error of line features, we introduce a new error term that leverages the geometric constraints of plane normal vectors formed by matched line features and the optical center of the RGB-D camera. Furthermore, we estimate the pose of the RGB-D camera by loosely coupling point-line visual features with IMU pre-integration measurements. In the back end of VID-SLAM, we tightly couple the point-line feature error model with the IMU pre-integration to jointly optimize the camera pose. Extensive qualitative and quantitative comparisons demonstrate that our VID-SLAM algorithm achieves robust performance and comparable accuracy.

Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, this paper presents a novel line feature matching approach grounded in point–line invariants through spatial invariant relationships. By leveraging a robust point feature matching algorithm, an initial set of point feature matches is acquired. Subsequently, the line feature supporting area is partitioned, and a constant ratio invariant is formulated based on the distances from point to line features within corresponding neighborhood domains. Additionally, a direction vector invariant is also introduced, jointly constructing a dual invariant for line matching. An initial matching matrix and line feature match pairs are derived using this dual invariant. Subsequent geometric constraints within line feature matches eliminate residual outliers. Comprehensive evaluations under diverse imaging conditions, along with comparisons to several state-of-the-art algorithms, demonstrate that our proposal achieved remarkable performance in terms of both accuracy and robustness. Our implementation code will be publicly released upon the acceptance of this paper.