KMOP-vSLAM: Dynamic Visual SLAM for RGB-D Cameras using K-means and OpenPose

To solve the accurate positioning problem of mobile robots, simultaneous localization and mapping (SLAM) or visual odometry (VO) based on visual information are widely used. However, most visual SLAM or VO cannot meet the accuracy requirements in dynamic indoor environments. This paper proposes a robust visual odometry based on deep learning to eliminate feature points matching error. However, when a camera and dynamic objects are in relative motion, the frames of camera will produce ghosting, especially in high-dynamic environments, which bring additional positioning error; in view of this problem, a novel method based on the average optical flow value of the dynamic region is proposed to identify feature points of the ghosting, and then the feature points of the ghosting and dynamic region are removed. After the remaining feature points are matched, we use a non-linear optimization method to calculate the pose. The proposed algorithm is tested on TUM RGB-D dataset, and the results show that our VO improves the positioning accuracy than other robust SLAM or VO and is strongly robust especially in high-dynamic environments.

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

Current visual simultaneous localization and mapping (SLAM) systems have demonstrated commendable efficacy in static environments. However, the presence of dynamic objects in real-world settings frequently leads to system discrepancies, significantly impairing the accuracy and robustness of SLAM systems. Conventional visual SLAM approaches typically utilize epipolar constraints to mitigate the impact of outliers; nevertheless, they encounter limitations when confronted with a substantial number of dynamic or planar moving objects. To tackle these issues, this paper introduces a novel dynamic visual SLAM system, termed DEG-SLAM. Initially, the system employs the YOLOv5 object detection network to identify dynamic objects, subsequently relaying the semantic information to the tracking module. During the tracking phase, both semantic information and epipolar constraints are leveraged to filter out dynamic feature points. To address the challenges posed by the malfunctioning of epipolar constraints in degenerate scenes, DEG-SLAM incorporates a degenerate constraint mechanism aimed at further eliminating dynamic feature points. Furthermore, a reprojection constraint has been introduced to enhance the filtering of absent dynamic feature points that lie outside the detection boxes. Experimental findings reveal that DEG-SLAM significantly improves accuracy and robustness when compared to traditional ORB-SLAM3 in dynamic environments. The performance benefits of DEG-SLAM are particularly evident in degenerate scenarios, thereby affirming its practicality and reliability in complex settings.

Simultaneous Localization and Mapping (SLAM) plays an important role in the computer vision and robotics field. The traditional SLAM framework adopts a strong static world assumption for analysis convenience. How to cope with dynamic environments is of vital importance and attracts more attentions. Existing SLAM systems toward dynamic scenes either solely utilize semantic information, solely utilize geometry information, or naively combine the results from them in a loosely coupled way. In this paper, we present SOF-SLAM: Semantic Optical Flow SLAM, a visual semantic SLAM system toward dynamic environments, which is built on RGB-D mode of ORB-SLAM2. A new dynamic features detection approach called semantic optical flow is proposed, which is a kind of tightly coupled way and can fully take advantage of feature's dynamic characteristic hidden in semantic and geometry information to remove dynamic features effectively and reasonably. The pixel-wise semantic segmentation results generated by SegNet serve as mask in the proposed semantic optical flow to get a reliable fundamental matrix, which is then used to filter out the truly dynamic features. Only the remaining static features are reserved in the tracking and optimization module to achieve accurate camera pose estimation in dynamic environments. Experiments on public TUM RGB-D dataset and in real-world environment are conducted. Compared with ORB-SLAM2, the proposed SOF-SLAM achieves averagely 96.73% improvements in high-dynamic scenarios. It also outperforms the other four state-of-the-art SLAM systems which cope with the dynamic environments.

The SLAM (Simultaneous Localization and Mapping) problem is one of the essential challenges for the current robotics. Our main objective in this work is to develop a real-time visual SLAM system using monocular omnidirectional vision. Our approach is based on the Extended Kalman Filter (EKF). We use the Spherical Camera Model to obtain geometric information from the images. This model is integrated in the EKF-based SLAM through the linearization of the direct and the inverse projections. We introduce a new computation of the descriptor patch for catadioptric omnidirectional cameras which aims to reach rotation and scale invariance. We perform experiments with omnidirectional images comparing this new approach with the conventional one. The experimentation confirms that our approach works better with omnidirectional cameras since features last longer and constructed maps are bigger.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or feature-based methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing high-level information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learning-based techniques. The system is built on ORB-SLAM2, a state-of-the-art SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios.

Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms.

Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to dynamic real-world environments. The correctness of re-localization and recall of loop closure detection are both lower when the mobile robot loses frames in a dynamic environment. Thus, in this paper, the re-localization and loop closure detection method with a semantic topology graph based on ORB-SLAM2 is proposed. First, we use YOLOv5 for object detection and label the recognized dynamic and static objects. Secondly, the topology graph is constructed using the position information of static objects in space. Then, we propose a weight expression for the topology graph to calculate the similarity of topology in different keyframes. Finally, the re-localization and loop closure detection are determined based on the value of topology similarity. Experiments on public datasets show that the semantic topology graph is effective in improving the correct rate of re-localization and the accuracy of loop closure detection in a dynamic environment.

This paper proposes an appearance-based method to detect loop closure in visual SLAM (Simultaneous Localization and Mapping). To solve this problem, we make use of omnidirectional images and the internal odometry captured by a robot in a real indoor environment. We build an appearance-based model and, subsequently, two maps of the environment are constructed, one metric and other topological with relationships between them. These relationships are updated in each step of our hybrid approach. The topological map is a graph built from the appearance information in the scenes. A new node is added when the new visual information is different enough from the previous information. At the same time, we check a possible topological loop closure with previous nodes. On the other hand we estimate the metric position of the new pose using a Monte-Carlo approach with the aim of building a metric map. The experimental results demonstrate the reasonable performance of our method.KeywordsAppearance-base descriptorOmnidirectional ImagesMonte-Carlo SLAMHybrid Metric-Topological mappingLoop closure detection

Simultaneous Localization and Mapping (SLAM) technology can help mobile intelligent robots to understand and perceive scenes in unknown environments, so it is playing an increasingly important role in the fields of intelligent robots, intelligent cars, and so on. And because camera sensors have wide applicability, visual SLAM in dynamic scenes has become a relatively popular research direction in recent years. And the SLAM algorithm needs to be tested and validated, which requires choosing appropriate datasets based on different application scenarios. Therefore, this paper comprehensively introduces the excellent open-source data sets commonly used in the research of visual SLAM. Since deep learning networks are often used to improve the performance of the SLAM system, this paper summarizes the advanced techniques in recent years for solving the common problems of visual SLAM in dynamic scenes based on deep learning networks.<br />

Simultaneous Localization and Mapping (SLAM) is critical for autonomous driving. Compared with visual SLAM which suffers from variable lighting conditions and sparse features, Light Detection and Ranging (LiDAR) based solution is highly competitive since LiDAR can offer precise distance measurements and robust resistance to changes in illumination. However, current LiDAR SLAM techniques only estimate pose from a single scale, such that complex environments with dynamic objects and similar features can heavily affect the performance, leading to poor accuracy and robustness of pose estimation. To address these challenges, we present MTC-SLAM, a multi-scale and tightly coupled LiDAR-Inertial SLAM method. It begins with a coarse adjustment across six degrees of freedom at a larger scale, followed by adaptive step-size least squares updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{y}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{yaw}$</tex-math></inline-formula> on a smaller scale, and dynamic interpolation updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{z}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{pitch}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{roll}$</tex-math></inline-formula>. The updated poses are integrated as odometer factors, alongside Global Positioning System (GPS) and loop closure factors, to construct a factor graph for backend optimization. Additionally, a multi-scale loop closure detection method is proposed to identify loops using multi-scale descriptors for global optimization. Extensive experiments on the MulRan dataset demonstrate that MTC-SLAM outperforms existing mainstream methods in both accuracy and robustness.

Visual simultaneous localization and mapping (VSLAM) is extensively utilized in the field of unmanned driving. Classical VSLAM typically postulates that the surroundings are stationary, and dynamic objects can seriously affect the accuracy of the system. However, detecting and tracking dynamic objects is beneficial for improving the autonomy and intelligence of robotics. In order to solve the problem of VSLAM distinguishing dynamic objects in dynamic scenes, a semantic VSLAM system based on the VDO-SLAM framework is proposed. The approach integrates semantic segmentation into the visual odometry and combines scene flow and geometric constraints to effectively reduce the negative impact of dynamic objects on system performance. Specifically, a transformer-based neural network architecture for optical flow estimation was employed, which effectively enhances the segmentation precision of foreground and background regions. And a geometric constraint was introduced to classify dynamic objects, which can reduction of erroneous segmentation of dynamic objects with effect. The data analysis results on the KITTI dataset demonstrate that our proposed algorithms can effectively enhance precision and stability of VSLAM system, reduce the tracking error of VSLAM in dynamic environments, and demonstrate good potential and application value.

Typical visual simultaneous localization and mapping (SLAM) systems rely on purely geometric elements. Recently semantic SLAM has become popular due to its capability to obtain high-level understanding of the environment. However, most semantic SLAM systems merely aim to produce semantic maps rather than exploiting semantic constraints to boost the accuracy performance. In this paper, we propose a new RGB-D SLAM system for the use in indoor environments. Given that a ground plane represents a significant part of most indoor scenes, our new system exploit a semantic ground plane constraint inferred from a deep neural network along with geometric constraints on the state estimation to improve the performance. More specifically, we introduce a semantically meaningful geometric primitive, namely, a global ground plane to the optimization process of visual SLAM. The global ground model is able to provide an extra constraint on the state estimation through which both frame-to-keyframe and frame-to-model tracking processes are combined, and low-texture ground plane is effectively utilized. Experimental results for the TUM dataset and a real scene have demonstrated the improved performance brought by the semantic constraint in our method.

Simultaneous localization and mapping (SLAM) is a basic prerequisite in autonomous mobile robotics. Most existing visual SLAM approaches either assume a static environment, or simply ’forget’ old parts of the map to cope with map size constraints and scene dynamics. We present a novel map representation for sparse visual features. A new 3D point descriptor called Histogram of Oriented Cameras (HOC) encodes anisotropic spatial visibility information and the importance of each three-dimensional landmark. Each feature holds and updates a histogram of the poses of observing cameras. It is hereby able to estimate its probability of occlusion and importance for localization from a given viewpoint. In a series of simulated and real-world experiments we prove that the proposed descriptor allows to cope with dynamic changes in the map, improves localization accuracy and enables reasonable control of the map size.

Visual Simultaneous Localization and Mapping (SLAM) plays an important role in computer vision and robotic field. With the development of Convolutional Neural Network (CNN), most scholars currently fuse CNN with visual SLAM to reduce the impact of dynamic objects on visual SLAM. To address the impact of semantic segmentation networks with lower precision and quad-tree algorithm with over-uniform distribution of feature points on the location accuracy of SLAM, we proposed an STDC-SLAM: Short-Term Dense Concatenate Network SLAM, which was based on ORB-SLAM3. In the proposed system, a real-time STDC network was used for semantic thread to segment dynamic objects. On the one hand, we designed a segmentation refinement module to optimize the semantic segmentation maps using images depth information. On the other hand, we improved the Qtree-ORB algorithm by reducing the iterations of low-quality feature points in the rejection thread. We have evaluated our SLAM in public data sheets and compared it with ORB-SLAM3, DynaSLAM, PSPNet-SLAM. Experiments showed that our SLAM improved in localization accuracy compared to DynaSLAM and in processing speed compared to DynaSLAM and PSPNet-SLAM.