Simultaneous Localization And Mapping Method Research Articles

METHOD OF SIMULTANEOUS LOCALIZATION AND MAPPING FOR CONSTRUCTION OF 2.5D MAPS OF THE ENVIRONMENT USING ROS

SLAM (Simultaneous Localization and Mapping) is a relevant topic of research and development in the field of robotics and computer vision. SLAM finds wide applications in various areas such as autonomous navigation of intelligent robots, solving problems in augmented and virtual reality, UAVs, and other systems. In recent years, SLAM has made significant progress due to the gradual development of its algorithms, the use of advanced sensors, and improvements in computational power of computers. The subject of this study is modern methods of real-time simultaneous localization and mapping. The goal of the research is to model the developed algorithm for constructing maps of the surrounding environment and determining the position and orientation of the intelligent robot in space in real-time using ROS packages. The purpose of this article is to demonstrate the results of combining SLAM methods and developing new approaches to solve simultaneous localization and mapping problems. In order to achieve the set objectives, a collaboration of laser scanning (2D LRF) and depth image reconstruction (RGB-D) methods was utilized for simultaneous localization and mapping of the intelligent robot and construction of a 2.5D environment map. The obtained results are promising and demonstrate the potential of the integrated SLAM methods, which collaborate to ensure accurate execution of simultaneous localization and mapping for intelligent robots in real-time mode. The proposed method allows for considering obstacle heights in constructing the map of the surrounding environment while requiring less computational power. In conclusion, this approach expands technologies without replacing existing working propositions and enables the use of modern methods for comprehensive detection and recognition of the surrounding environment through an efficient localization and mapping approach, providing more accurate results with fewer resources utilized.

A LiDAR-intensity SLAM and loop closure detection method using an intensity cylindrical-projection shape context descriptor

LiDAR Simultaneous Localization and Mapping (SLAM) is able to map unknown scene while online estimating the LiDAR’s pose. Traditional LiDAR SLAM frameworks mainly rely on geometric features in the environments, but ignore LiDAR intensity information, leading to low accuracy in scenes with sparse environmental features. This paper proposes a novel intensity-based LiDAR-SLAM framework. In the front-end, the geometric and intensity features are extracted to match the two consecutive scans. To keep time efficiency, a self-adaptive feature selection strategy is proposed to select geometric and intensity features and the 6 DOF transformation from the corresponding features are weighted fused for odometry estimation. To improve the effectiveness of loop closure detection (LCD), we propose a novel intensity cylindrical-projection shape context (ICPSC) descriptor and a row-column similarity estimation based on ICPSC. To guarantee the accuracy of LCD, a double-value loop candidate verification strategy is employed. We have conducted comprehensive experimental verification of our LiDAR SLAM framework, including both the public KITTI datasets and data from our platform that involve multiple scenes. The results display that our framework achieves an averaged improvement of about 3 m in scenes with sparse environmental features in terms of on-line localization relative to LeGO-LOAM, while only increases maximum 9 ms in terms of time cost.

An effective stereo SLAM with high-level primitives in underwater environment

Visual simultaneous localization and mapping (SLAM) algorithms face challenges in complex underwater scenarios, such as turbidity, dynamism, and low texture, where point features are unreliable and can lead to weakened or even failed systems. To overcome these issues, high-level object features are considered due to their accuracy and robustness. In this paper, we introduce an effective object-level SLAM method that employs a stereo camera to enhance the navigation robustness of autonomous underwater vehicles and generates a detailed semantic map. Point features and object features are integrated to serve the proposed approach. We begin by detecting 2D objects in images using a state-of-the-art neural network, followed by obtaining 3D objects described by the general model through the principle of multi-view geometry and eventually constructing semantic landmarks. To account for object data association, we present an object match method that takes into consideration the stereo camera characteristics in a single stereo frame and a filter-based approach to track the landmarks in odometry. Experiments are also conducted using the KITTI dataset and our sequences collected from the pool and coast. The evaluation results indicate that the proposed method can improve the performance of ORBSLAM2 in terms of both navigation robustness and mapping information in underwater scenarios.

Research on orchard navigation method based on fusion of 3D SLAM and point cloud positioning.

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5cm with a standard deviation of less than 2cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.

Visual–Inertial Navigation System Based on Virtual Inertial Sensors

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.

Three-Dimensional Location and Mapping Analysis in Mobile Robotics Based on Visual SLAM Methods

One of the essential tasks required from a mobile robot is the autonomous and safe navigation of its working environment. However, in many cases, a model of the environment or map is not available to execute this task. Indeed, navigation requires a permanent estimation of the location for a map, which is not available for unknown environments. In such a scenario, the robot must have extended capabilities to solve, concurrently, the problems of localization and mapping. The simultaneous solution of these two problems is known as SLAM (simultaneous localization and mapping) and is a complex problem, not yet fully solved by the scientific community. This is due to the fact that localization requires a map that is not yet available since it is still under construction. In turn, the elaboration of a map requires the estimation of the robot’s location. This is the reason why SLAM has been categorized as similar to the chicken and egg problem. In the case of a robot facing an unknown environment, it would be something like what to solve first, localization or mapping? The answer to this question is that the robot will have to solve both problems at the same time. This article presents a study of some of the most representative open source visual SLAM (vSLAM) methods, beginning from an analysis of their characteristics and presenting criteria selection for an experimental design that allows contrasting their advantages and disadvantages. Two of the most representative algorithms for solving vSLAM were considered (RTAB-Map and ORB-SLAM2). The experiments were validated with a robotic system designed for this purpose, which is fully compatible with ROS (robot operating system).

OVD-SLAM: An Online Visual SLAM for Dynamic Environments

Most existing dynamic simultaneous localization and mapping (SLAM) methods integrating neural networks require high computational support and predefined categories of dynamic objects. This article proposes an online visual SLAM system without predefined dynamic labels for dynamic environments, which is built on ORB-SLAM3. We combine semantic information, depth information, and optical flow to distinguish foreground and background and recognize dynamic regions. The foreground points in dynamic areas are regarded as moving points. We also restore static points on moving objects by the average reprojection error between multiple frames to adapt to nonrigid motion and low-dynamic environments. In pose optimization, we define an optimization weight for every point to decrease the negative influence of potential dynamic points. The experiments on TUM RGB-D and Bonn datasets show that OVD-SLAM achieves more accurate and robust localization than other state-of-art dynamic SLAM methods. To further verify our system’s online performance and robustness, we also test it in real dynamic scenes and apply it to an augmented reality (AR) application using a handheld RGB-D camera.

A LiDAR–Inertial SLAM Method Based on Virtual Inertial Navigation System

In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.

A comprehensive computational cost analysis for state-of-the-art visual slam methods for autonomous mapping

It is important to solve the autonomous mapping problem with high accuracy using limited energy resources in an environment without prior knowledge and/or signal. Visual Simultaneous Localization and Mapping (SLAM) deals with the problem of determining the position and orientation of an autonomous vehicle or robot with various on-board sensors, and simultaneously creating a map of environment with low energy consumption. However visual SLAM methods require high processing performance for real-time operations. Also, processing capability of the hardware is limited by the power constraints. Therefore, it is necessary to compare the processing load and power consumption of visual SLAM methods for autonomous vehicles or robots. For visual SLAM methods, although there are different comparison studies, there is no comprehensive computational cost analysis covering different datasets and important parameters including absolute trajectory error, RAM Usage, CPU load, GPU load, with total power consumption. In this paper, ORB-SLAM2, Direct Sparse Odometry (DSO), and DSO with Loop Closure (LDSO), which are state of the art visual SLAM methods, are compared. Besides the performance of these methods, energy consumption and resource usage are evaluated allowing the selection of the appropriate SLAM method.

A High-Precision LiDAR-Inertial Odometry via Kalman Filter and Factor Graph Optimization

For simultaneous localization and mapping (SLAM) in large-scale scenarios, the influence of long-distance and high-speed motion cannot be ignored, because the risk of huge odometry drift will increase. To solve this problem, we propose the LiDAR-Inertial odometry (LIO) method via Kalman filter and factor graph optimization (LIO-FILO), which provides real-time, high-frequency, and high-precision odometry. The LIO system consisting of three modules is established to fit various application scenarios. The state estimation module provides the pose states to be optimized and receives timely feedback from the pose optimization module. In the loop closure module, LIO-FILO constructs a multilayer structure loop closure detection method including different schemes of detection, and the loop closure factor is constructed by the pose transformation matrix calculated by ICP. In the pose optimization module, the fast-build adjacent constraint factors and the loop closure factors are added to the factor graph to get the optimized result based on the GTSAM library. The real-world experiments show that LIO-FILO can mitigate the odometry drift to achieve accurate mapping results, and obtain higher precision odometry compared with the existing advanced SLAM methods.

Comparing LiDAR and IMU-based SLAM approaches for 3D robotic mapping

AbstractIn this paper, we propose a comparison of open-source LiDAR and Inertial Measurement Unit (IMU)-based Simultaneous Localization and Mapping (SLAM) approaches for 3D robotic mapping. The analyzed algorithms are often exploited in mobile robotics for autonomous navigation but have not been evaluated in terms of 3D reconstruction yet. Experimental tests are carried out using two different autonomous mobile platforms in three test cases, comprising both indoor and outdoor scenarios. The 3D models obtained with the different SLAM algorithms are then compared in terms of density, accuracy, and noise of the point clouds to analyze the performance of the evaluated approaches. The experimental results indicate the SLAM methods that are more suitable for 3D mapping in terms of the quality of the reconstruction and highlight the feasibility of mobile robotics in the field of autonomous mapping.

A real-time semantic visual SLAM for dynamic environment based on deep learning and dynamic probabilistic propagation

Most existing visual simultaneous localization and mapping (SLAM) algorithms rely heavily on the static world assumption. Combined with deep learning, semantic SLAM has become a popular solution for dynamic scenes. However, most semantic SLAM methods show poor real-time performance when dealing with dynamic scenes. To handle this problem, a real-time semantic SLAM method is proposed in this paper, combining knowledge distillation and dynamic probability propagation strategy. First, to improve the execution speed, a multi-level knowledge distillation method is adopted to obtain a lightweight segmentation model, which is more suitable for continuous frames to create an independent semantic segmentation thread. This segmentation thread only accepts keyframes as input so that the system can avoid time delay caused by processing each frame. Second, a static semantic keyframe selection strategy is proposed based on the segmentation results. In this way, those keyframes containing more static information will be selected to reduce the participation of dynamic objects. By combining segmentation results and data matching algorithm, our system can realize the update and propagation of dynamic probability, reducing the influence of dynamic points in the pose optimization process. Validation results based on the KITTI and TUM datasets show that our method can effectively deal with dynamic feature points and improve running speed simultaneously.

OSM-SLAM: Aiding SLAM with OpenStreetMaps priors.

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.

A visual SLAM algorithm based on fuzzy clustering for removing dynamic features

Most visual simultaneous localization and mapping (SLAM) algorithms assume that no or only few moving objects occur in application environments. This assumption makes the algorithms vulnerable to the interference of moving objects in dynamic environments. To address the problem, a new visual SLAM method, which could eliminate dynamic features without any prior information, was proposed. By measuring the position of each feature point and its motion vector difference between image sequences, a two-stage clustering was performed on the feature points in the field of view. This method removed the features detected on moving objects, and used a static initialization technique to eliminate the dependence of SLAM on prior information. The proposed method intended to improve OV2SLAM (a fully online and versatile visual SLAM for real-time applications) algorithm, and the experimental verification was carried out. Our results show that while maintaining the real-time performance of the original OV2SLAM algorithm, the positioning accuracy and robustness of the proposed method is improved in a dynamic environment.

UPLP-SLAM: Unified point-line-plane feature fusion for RGB-D visual SLAM

Most of the existing RGB-D simultaneous localization and mapping (SLAM) systems are based on point features or point-line features or point-plane features. However, the existing multi- feature fusion SLAM methods based on the filter framework are not accurate and robust. And the fusion methods based on the optimization framework process different kinds of features separately and integrate them loosely. In the optimization-based framework, how to tightly fuse various kinds of features for achieving more accurate and robust pose estimation has been rarely considered. In this paper, we propose a unified point-line-plane fusion RGB-D visual SLAM method for navigation of mobile robots in structured environments, making full use of the information of three kinds of geometric features. Specifically, it extracts point, line, and plane features using images captured by the RGB-D camera and expresses them in a uniform way. Then, a mutual association scheme is designed for data association of point, line and plane features, which not only considers the correspondence of homogeneous features, i.e., point-point, line-line and plane-plane pairs, but also includes the association of heterogeneous features, i.e., point- line, point-plane and line-plane pairs. Afterwards, the matching errors between homogeneous features and the association errors between heterogeneous features are uniformly represented and jointly optimized to estimate the camera pose and feature parameters for accurate and consistent localization and map building. It is worth pointing out that the proposed unified framework contains two levels. From the system framework perspective, all the main components of the SLAM system, i.e., feature representation, feature association and error function are handled in a unified manner, which increases the accuracy and compactness of the multi-feature SLAM system. From the feature processing perspective, both homogeneous features and heterogeneous features are uniformly used, which provides more spatial constraints on pose estimation. Finally, the accuracy and robustness of the proposed method are verified by experiment comparisons with state-of-the-art visual SLAM systems on public datasets and in real-world environments

Long-Term Visual Simultaneous Localization and Mapping: Using a Bayesian Persistence Filter-Based Global Map Prediction

With the rapidly growing demand for accurate localization in real-world environments, visual simultaneous localization and mapping (SLAM) has received significant attention in recent years. However, those existing methods still suffer from the degradation of localization accuracy in long-term changing environments. To address these problems, we propose a novel long-term SLAM system with map prediction and dynamics removal. First, a visual point-cloud matching algorithm is designed to efficiently fuse 2D pixel information and 3D voxel information. Second, each map point is classified into three types: static, semistatic, and dynamic based on the Bayesian persistence filter (BPF). Then we remove the dynamic map points to eliminate the influence of those map points. We can obtain a global predicted map by modeling the time series of semistatic map points. Finally, we incorporate the predicted global map into a state-of-the-art SLAM method, achieving an efficient visual SLAM system for long-term, dynamic environments. Extensive experiments are carried out on a wheelchair robot in an indoor environment over several months. The results demonstrate that our method has better map prediction accuracy and achieves more robust localization performance.

An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments

The visual simultaneous localization and mapping (SLAM) method under dynamic environments is a hot and challenging issue in the robotic field. The oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one of the most effective methods. However, the traditional ORB-SLAM algorithm cannot perform well in dynamic environments due to the feature points of dynamic map points at different timestamps being incorrectly matched. To deal with this problem, an improved visual SLAM method built on ORB-SLAM3 is proposed in this paper. In the proposed method, an improved new map points screening strategy and the repeated exiting map points elimination strategy are presented and combined to identify obvious dynamic map points. Then, a concept of map point reliability is introduced in the ORB-SLAM3 framework. Based on the proposed reliability calculation of the map points, a multi-period check strategy is used to identify the unobvious dynamic map points, which can further deal with the dynamic problem in visual SLAM, for those unobvious dynamic objects. Finally, various experiments are conducted on the challenging dynamic sequences of the TUM RGB-D dataset to evaluate the performance of our visual SLAM method. The experimental results demonstrate that our SLAM method can run at an average time of 17.51 ms per frame. Compared with ORB-SLAM3, the average RMSE of the absolute trajectory error (ATE) of the proposed method in nine dynamic sequences of the TUM RGB-D dataset can be reduced by 63.31%. Compared with the real-time dynamic SLAM methods, the proposed method can obtain state-of-the-art performance. The results prove that the proposed method is a real-time visual SLAM, which is effective in dynamic environments.

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5-10 cm mapping accuracy and 20-30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.