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

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.

Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV allows for a wider range of surrounding elements and features to be perceived. However, when the FoV of the camera reaches the negative half-plane, traditional methods for representing image feature points using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[u,v,1]^T$</tex-math> </inline-formula> become ineffective. While the panoramic FoV is advantageous for loop closure, its benefits are not easily realized under large-attitude-angle differences where loop-closure frames cannot be easily matched by existing methods. As loop closure on wide-FoV panoramic data further comes with a large number of outliers, traditional outlier rejection methods are not directly applicable. To address these issues, we propose LF-VISLAM, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> isual <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nertial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLAM</b> framework for cameras with extremely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> arge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> oV with loop closure. A three-dimensional vector with unit length is introduced to effectively represent feature points even on the negative half-plane. The attitude information of the SLAM system is leveraged to guide the feature point detection of the loop closure. Additionally, a new outlier rejection method based on the unit length representation is integrated into the loop closure module. We collect the PALVIO dataset using a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> anoramic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nnular <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> ens (PAL) system with an entire FoV of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^\circ{\times}(40^\circ{\sim}120^\circ)$</tex-math> </inline-formula> and an Inertial Measurement Unit (IMU) for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> isual <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nertial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> dometry (VIO) to address the lack of panoramic SLAM datasets. Experiments on the established PALVIO and public datasets show that the proposed LF-VISLAM outperforms state-of-the-art SLAM methods. Our code will be open-sourced at https://github.com/flysoaryun/LF-VISLAM. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the challenges of handling large-FoV cameras in SLAM applications, this paper proposes LF-VISLAM, a novel SLAM framework that uses a large-FoV camera and IMU sensors. Our framework is equipped with a loop closure thread that can use attitude information to eliminate accumulated errors. We have made algorithmic adjustments and optimizations to the negative half-plane features to better adapt to cameras with large FoV. Experimental evaluations demonstrate that LF-VISLAM significantly outperforms traditional SLAM methods. Additionally, the code will be open-sourced, providing easy access for research and implementation. Overall, LF-VISLAM is a promising solution to improve the performance of SLAM in challenging environments of autonomous driving and robotics.

The Autonomous Modular Bus (AMB) introduces an innovative approach to public transportation by allowing modular buses to dock and undock seamlessly while in motion. This capability effectively alleviates traffic congestion and decreases energy usage through smoother and more efficient vehicle operation. However, achieving autonomous docking for AMBs poses significant challenges, including the need for precise localization in both horizontal and vertical dimensions and the ability to manage dynamic persistent obstacles in close-range scenarios. Existing Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) algorithms, such as LIO-SAM, perform well in static environments but encounter limitations in dynamic scenarios, particularly with occlusions and vertical drift during AMB docking. In this paper, we propose an enhanced LiDAR-Inertial Measurement Unit (IMU) SLAM framework focused on improving localization accuracy and robustness during AMB docking. Key contributions include: (1) A two-stage scan-to-map matching method with ground constraints to reduce z-axis drift; (2) A factor graph optimization strategy integrating IMU roll and pitch constraints and periodic resetting to mitigate long-term drift; (3) A deep learning-based front vehicle detection and point cloud filtering mechanism to reduce occlusion effects. Experimental evaluations on single-vehicle and dual-vehicle datasets demonstrate that our method significantly reduces Absolute Pose Error (APE) and Relative Pose Error (RPE) compared to existing methods. These results highlight the framework's ability to address the unique challenges of AMB docking, therefore helping alleviate traffic congestion and reduce energy consumption.

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

Loop closure detection (LCD) is an important module of visual SLAM (Simultaneous Localization and Mapping), which can be regarded as an image retrieval task. The correct LCD can effectively reduce the cumulative errors during visual SLAM localization and obtain a globally consistent map. The currently common approach to LCD is based on a bag-of-words (BoW) and traditional manually designed features. However, The BoW-based method ignores the spatial location information and semantic information of visual features, and image aliasing will affect the detection accuracy of the algorithm. Meanwhile, the artificially designed features are very sensitive to light changes. This paper proposes a LCD algorithm based on convolutional neural networks (CNN) to extract global and local features. Extensive experiments on six publicly available datasets have shown that the method provides high recall with 100% accuracy in most of the evaluated datasets relative to traditional algorithms and some deep learning-based methods.

Simultaneous Localization and Mapping (SLAM) systems face severe challenges in dynamic environments, where the presence of moving objects (e.g., pedestrians) significantly degrades localization accuracy and map consistency. To address this issue, this paper proposes a robust SLAM framework for dynamic environments that integrates semantic perception and geometric constraints. The method focuses on two key improvements: first, a deep visual semantic detection network is used to identify and model dynamic objects in the scene, automatically eliminating unstable feature points caused by moving objects to reduce dynamic interference and enhance the robustness of pose estimation; second, semantic detection results are fused with depth information into a voxel mapping module, and an improved TSDF (Truncated Signed Distance Function) mapping process is adopted to achieve semantic annotation of dynamic objects and spatial filtering. Experimental results show that in highly dynamic scenes, the proposed method can significantly reduce trajectory estimation errors-the Root Mean Square Error (RMSE) of Absolute Trajectory Error (ATE) is reduced by approximately 90 % compared with traditional SLAM sys-tems-effectively suppressing localization drift. Meanwhile, the semantic-enhanced mapping module generates a more accurate, complete, and semantically distinguishable 3D map, providing reliable support for autonomous perception and decision-making in complex dynamic environments.

Simultaneous localization and mapping (SLAM) for mobile robots has attracted research interests in the past two decades. Recent years, Rao-Blackwellized particle filter (RBPF) approach proved to be an effective means to estimate the full SLAM posterior. However, most of the SLAM practices are implemented in static environment. To navigate in dynamic environment, model based approaches have been implemented. The approaches require modeling, classification, and data association. Modeling and classification often require prior knowledge of the environment and are less robust in highly complex environment. To obviate modeling, classification and data association in dynamic environment, a solution framework based on Rao-Blackwellised genetic algorithmic filter (RBGAF) is extended for recovering the full SLAM posterior, using raw sensor measurements. The resultant extended RBGAF-SLAM recovers the negative sensor information during SLAM so that the moving objects can be efficiently identified in the particle maps. This approach permits the implementation of SLAM without prior knowledge of the environment and the static environment assumption. Simulations and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

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.

This work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time, with a complexity that does not depend on the size of the global model. At the same time, it is also general, as it can be deployed with any frame-wise segmentation approach as well as any SLAM algorithm. We validate our proposal by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how our method can enable real-time segmentation from reconstructions of diverse real indoor environments.

Simultaneous localization and mapping (SLAM) is one of the cornerstones of autonomous navigation systems in robotics and the automotive industry. Visual SLAM (V-SLAM), which relies on image features, such as keypoints and descriptors to estimate the pose transformation between consecutive frames, is a highly efficient and effective approach for gathering environmental information. With the rise of representation learning, feature detectors based on deep neural networks (DNNs) have emerged as an alternative to handcrafted solutions. This work examines the integration of sparse learned features into a state-of-the-art SLAM framework and benchmarks handcrafted and learning-based approaches by comparing the two methods through in-depth experiments. Specifically, we replace the ORB detector and BRIEF descriptor of the ORBSLAM3 pipeline with those provided by Superpoint, a DNN model that jointly computes keypoints and descriptors. Experiments on three publicly available datasets from different application domains were conducted to evaluate the pose estimation performance and resource usage of both solutions.

Event-based visual odometry is a specific branch of visual simultaneous localization and mapping (SLAM) techniques, which aims at solving tracking and mapping subproblems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline viewpoint changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping subproblem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this article, we tackle these issues by building an event-based stereo visual-inertial odometry system, which is built upon a direct pipeline known as event-based stereo visual odometry (ESVO). Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via preintegration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods. Compared to ESVO, our new pipeline significantly reduces the camera pose tracking error by 40%–80% and 20%–80% in terms of absolute trajectory error and relative pose error, respectively; at the same time, the mapping efficiency is improved by a factor of five. We release our pipeline as an open-source software for future research in this field.

Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGS, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA.

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.