SOF-SLAM: A Semantic Visual SLAM for Dynamic Environments

Localization and mapping in a dynamic scene is a crucial problem for the indoor visual simultaneous localization and mapping (SLAM) system. Most existed visual odometry (VO) or SLAM systems are based on the assumption that the environment is static. The performance of a SLAM system may degenerate when it is operated in a severely dynamic environment. The assumption limits the applications of RGB-D SLAM in the dynamic environment. In this paper, we propose a workflow to segment the objects accurately, which will be marked as the potentially dynamic-object area based on the semantic information. A novel approach for motion detection and removal from the moving camera is introduced. We integrate the semantics-based motion detection and the segmentation approach with an RGB-D SLAM system. To evaluate the effectiveness of the proposed approach, we conduct the experiments on the challenging dynamic sequences of TUM-RGBD datasets. The experimental results suggest that our approach improves the accuracy of localization and outperforms the state-of-the-art dynamic-removal-based SLAM system in both severely dynamic and slightly dynamic scenes.

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.

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.

The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

In indoor dynamic scenes, traditional visual simultaneous localization and mapping (SLAM) algorithms based on RGB-D cameras incorrectly use dynamic features to estimate the poses of the cameras, and do not fully utilize the geometric information in the scenes, resulting in the low positioning accuracy and robustness of SLAM systems. To solve this problem, this paper proposes an RGB-D SLAM algorithm based on multiple geometric features and semantic segmentation. The core of our SLAM system is the proposed robust exclusion method of dynamic point and line features. The method consists of the following three steps: (a) identify potential dynamic point features using motion consistency checks; (b) obtain potential motion regions via semantic segmentation, and then determine dynamic regions by combining these with dynamic point features; (c) remove point and line features in dynamic regions. The exclusion method of dynamic point and line features can be easily integrated into RGB-D SLAM systems for improving the accuracy and robustness of SLAM systems in dynamic scenes. Experimental results on the Technische Universität München (TUM) dataset demonstrate that the proposed algorithm has better positioning accuracy and stability than the original dynamic semantic (DS-SLAM) algorithm in dynamic environments. The effectiveness of the proposed algorithm is verified by comparison with other classical visual SLAM algorithms. Better mapping performance is achieved by this proposed algorithm in actual indoor scenes.

Augmented Reality (AR) has increasingly benefited from the use of Simultaneous Localization and Mapping (SLAM) systems. This technology has enabled developers to create AR markerless applications, but lack semantic understanding of their environment. The inclusion of this information would empower AR applications to better react to the surroundings more realistically. To gain semantic knowledge, in recent years, focus has shifted toward fusing SLAM systems with neural networks, giving birth to the field of Semantic SLAM. Building on existing research, this paper aimed to create a SLAM system that generates a 3D map using ORB-SLAM2 and enriches it with semantic knowledge originated from the Fast-SCNN network. The key novelty of our approach is a new method for improving the predictions of neural networks, employed to balance the loss of accuracy introduced by efficient real-time models. Exploiting sensor information provided by a smartphone, GPS coordinates are utilized to query the OpenStreetMap database. The returned information is used to understand which classes are currently absent in the environment, so that they can be removed from the network’s prediction with the goal of improving its accuracy. We achieved 87.40% Pixel Accuracy with Fast-SCNN on our custom version of COCO-Stuff and showed an improvement by involving GPS data for our self-made smartphone dataset resulting in 90.24% Pixel Accuracy. Having in mind the use on smartphones, the implementation aimed to find a trade-off between accuracy and efficiency, making the system achieve an unprecedented speed. To this end, the system was carefully designed and a strong focus on lightweight neural networks is also fundamental. This enabled the creation of an above real-time Semantic SLAM system that we called EnvSLAM (Environment SLAM). Our extensive evaluation reveals the efficiency of the system features and the operability in above real-time (48.1 frames per second with an input image resolution of 640 × 360 pixels). Moreover, the GPS integration indicates an effective improvement of the network’s prediction accuracy.

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 Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes, unreliable feature points in the scene will lead to the decline of system positioning accuracy and even cause system failure. Traditional methods often use the removal of dynamic points to solve dynamic scene problems, but in some environments, the reduction of feature points will also affect the positioning accuracy. Therefore, based on the ORB-SLAM2 visual SLAM system, this paper proposes a semantic point and line SLAM system for an indoor dynamic environment. The improved SLAM system has good performance in an indoor dynamic environment. Finally, we evaluate our algorithm on the TUM RGB-D dynamic dataset. The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.

Recently, Simultaneous Localization and Mapping (SLAM) has achieved good performance in a certain environment. However, in a complex indoor environment, the tracking of the position of the mobile robot in Visual SLAM may get lost in some scenes with hardly any features. This paper presents a mobile robot localization method using an asynchronous data fusion formed by a wheel odometer and a visual odometer in the Extended Kalman Filter (EKF) based on the Semantic SLAM system. The first approach uses Semantic SLAM based on ORB-SLAM2 with YOLOv3, to get rid of the dynamic object while localizing and mapping, and build a global point cloud map where keyframes information is stored. Then, wheel odometer information and visual odometer information acquire after using a global point cloud map. Finally, the asynchronous data fusion for mobile robot localization, composed of visual odometer and wheel odometer, is realized at a frequency of about 50Hz in EKF. The experimental results on the real robot validate the method in accuracy and robustness by comparing it with ground truth from the motion capture measurement system in indoor environment.

Although tremendous progress has been made in Simultaneous Localization and Mapping (SLAM), the scene rigidity assumption limits wide usage of visual SLAMs in the real-world environment of computer vision, smart robotics and augmented reality. To make SLAM more robust in dynamic environments, outliers on the dynamic objects, including unknown objects, need to be removed from tracking process. To address this challenge, we present a novel real-time visual SLAM system, KMOP-vSLAM, which adds the capability of unsupervised learning segmentation and human detection to reduce the drift error of tracking in indoor dynamic environments. An efficient geometric outlier detection method is proposed, using dynamic information of the previous frames as well as a novel probability model to judge moving objects with the help of geometric constraints and human detection. Outlier features belonging to moving objects are largely detected and removed from tracking. The well-known dataset, TUM, is used to evaluate tracking errors in dynamic scenes where people are walking around. Our approach yields a significantly lower trajectory error compared to state-of-the-art visual SLAMs using an RGB-D camera.

In this work, we present a comparative analysis of the trajectories estimated from various Simultaneous Localization and Mapping (SLAM) systems in a simulation environment for vineyards. Vineyard environment is challenging for SLAM methods, due to visual appearance changes over time, uneven terrain, and repeated visual patterns. For this reason, we created a simulation environment specifically for vineyards to help studying SLAM systems in such a challenging environment. We evaluated the following SLAM systems: LIO-SAM, StaticMapping, ORB-SLAM2, and RTAB-MAP in four different scenarios. The mobile robot used in this study equipped with 2D and 3D lidars, IMU, and RGB-D camera (Kinect v2). The results show good and encouraging performance of RTAB-MAP in such an environment.

In the last decades, Simultaneous Localization and Mapping (SLAM) proved to be a fundamental topic in the field of robotics, due to the many applications, ranging from autonomous driving to 3D reconstruction. Many systems have been proposed in literature exploiting a heterogeneous variety of sensors. State-of-the-art methods build their own map from scratch, using only data coming from the equipment of the robot, and not exploiting possible reconstructions of the environment. Moreover, temporary loss of data proves to be a challenge for SLAM systems, as it demands efficient re-localization to continue the localization process. In this paper, we present a SLAM system that exploits additional information coming from mapping services like OpenStreetMaps, hence the name OSM-SLAM, to face these issues. We extend an existing LiDAR-based Graph SLAM system, ART-SLAM, making it able to integrate the 2D geometry of buildings in the trajectory estimation process, by matching a prior OpenStreetMaps map with a single LiDAR scan. Each estimated pose of the robot is then associated with all buildings surrounding it. This association allows to improve localization accuracy, but also to adjust possible mistakes in the prior map. The pose estimates coming from SLAM are then jointly optimized with the constraints associated with the various OSM buildings, which can assume one of the following types: Buildings are always fixed (Prior SLAM); buildings surrounding a robot are movable in chunks, for every scan (Rigid SLAM); and every single building is free to move independently from the others (Non-rigid SLAM). Lastly, OSM maps can also be used to re-localize the robot when sensor data is lost. We compare the accuracy of the proposed system with existing methods for LiDAR-based SLAM, including the baseline, also providing a visual inspection of the results. The comparison is made by evaluating the estimated trajectory displacement using the KITTI odometry dataset. Moreover, the experimental campaign, along with an ablation study on the re-localization capabilities of the proposed system and its accuracy in loop detection-denied scenarios, allow a discussion about how the quality of prior maps influences the SLAM procedure, which may lead to worse estimates than the baseline.

Simultaneous localization and mapping (SLAM) is the basis for many robotic applications. Most SLAM algorithms are based on the assumption that the scene is static. In real-world applications, moving objects are inevitable, which will greatly impact the ego-pose estimation accuracy. This paper presents DyStSLAM, a visual SLAM system with a stereo configuration that can efficiently identify moving objects and accomplish dynamic data association. First of all, DyStSLAM extracts feature points, estimates the disparity map, and performs instance segmentation simultaneously. Then, the obtained results are combined to estimate the motion confidence and discriminate between moving objects and static ones. A confidence based matching algorithm is proposed to associate dynamic objects and estimate the pose of each moving object. At the same time, static objects are used to estimate the pose of the camera. Finally, after nonlinear optimization, a sparse point cloud map of both static background and dynamic objects is constructed. Compared with ORB-SLAM2, the proposed method outperforms by 31% for absolute trajectory error on the KITTI dataset.

Simultaneous Localization and Mapping (SLAM) is one of the fundamental functionalities for intelligent mobile robots. Over the last few decades, numerous impressive SLAM systems have been developed, demonstrating commendable performance in specific scenarios. However, some challenges still remain unsolved, such as how to handle moving objects in complex environments and how to enable robots to gain a true understanding of their surroundings and perform advanced tasks. This paper proposes a visual SLAM system, YOLOv7-SLAM, designed for complex environments with strong robustness. YOLOv7-SLAM employs a multi-threaded approach with five parallel threads: tracking, semantic segmentation, local mapping, loop closure, and dense semantic mapping creation. YOLOv7-SLAM employs the YOLO object detection method to remove obstacles in complex scenes, reducing the impact of obstacles in the environment and significantly enhancing localization accuracy in complex environments. We conducted experiments using the TUM RGB-D dataset. The results show that YOLOv7-SLAM achieves an order of magnitude improvement in absolute trajectory accuracy compared to ORB-SLAM2, making it one of the most advanced SLAM systems for complex environments. Our experiments were validated using the TUM RGB-D dataset. The results showcase a notable enhancement in the absolute trajectory accuracy of YOLOv7-SLAM in comparison to ORB-SLAM2 [1]. It performs well in complex obstacle-rich environments.

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.