Point Cloud Segmentation Research Articles

Geo-Net: Geometry-Guided Pretraining for Tooth Point Cloud Segmentation.

Accurately delineating individual teeth in 3-dimensional tooth point clouds is an important orthodontic application. Learning-based segmentation methods rely on labeled datasets, which are typically limited in scale due to the labor-intensive process of annotating each tooth. In this article, we propose a self-supervised pretraining framework, named Geo-Net, to boost segmentation performance by leveraging large-scale unlabeled data. The framework is based on the scalable masked autoencoders, and 2 geometry-guided designs, curvature-aware patching algorithm (CPA) and scale-aware reconstruction (SCR), are proposed to enhance the masked pretraining for tooth point cloud segmentation. In particular, CPA is designed to assemble informative patches as the reconstruction unit, guided by the estimated pointwise curvatures. Aimed at equipping the pretrained encoder with scale-aware modeling capacity, we also propose SCR to perform multiple reconstructions across shallow and deep layers. In vitro experiments reveal that after pretraining with large-scale unlabeled data, the proposed Geo-Net can outperform the supervised counterparts in mean Intersection of Union (mIoU) with the same amount of annotated labeled data. The code and data are available at https://github.com/yifliu3/Geo-Net.

A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation

This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. A key innovation is the development of the TJYRoad-Net model, which achieves over 85% mIoU segmentation accuracy by including a traffic feature computing (TFC) module composed of three critical components: the Regional Coordinate Encoder (RCE), the Context-Aware Aggregation Unit (CAU), and the Hierarchical Expansion Block. Comparative analysis segments the point clouds into road and non-road categories, achieving centimeter-level registration accuracy with RANSAC and ICP. Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. These methods ensure accurate results with minimal memory overhead. The optimized 3D models have been successfully applied in driving simulation and traffic flow analysis, providing a practical and scalable solution for real-world infrastructure modeling and analysis. These applications demonstrate the versatility and efficiency of the proposed methods in modern traffic system simulations.

Tightly Coupled LIDAR/IMU/UWB Fusion via Resilient Factor Graph for Quadruped Robot Positioning

Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs.

3D dataset generation using virtual reality for forest biodiversity

ABSTRACT While forest biodiversity faces a concerning decline, modern technology presents promising avenues for mitigation. However, a critical gap persists in reconciling ecological knowledge with the technical expertise required to use state-of-the-art technologies in 3D data classification. Currently, one main issue is the scarcity of 3D datasets for biodiversity, particularly within the context of machine learning applications. Unlike the straightforward classification of human-made structures, forest environments are uniquely intricate and nuanced due to its inherently complex nature. This study addresses this challenge by introducing a fully automated pipeline for tree stem 3D point cloud segmentation, focussing on a biodiversity indicator: tree-related microhabitats (TreMs). Furthermore, our research advances the field by demonstrating that machine learning models trained with labels generated by our proposed virtual reality (VR) method, Labelling Flora, yield predictions statistically similar to the traditional desktop-based labelling methods. This implies that existing 3D datasets could be augmented using the more rapid approach of VR labelling. Additionally, the findings of this paper demonstrate the potential integration of VR and immersive technology into the 3D labelling workflow, facilitating a quicker and more intuitive labelling process. This could empower users, who are non-familiar with 3D modelling, to contribute their expertise to the segmentation process.

Point cloud segmentation neural network with same-type point cloud assistance

This paper proposes neural network architectures for point cloud segmentation, which leverage prior knowledge derived from same-type point clouds. The approach involves concurrent processing of two point clouds: a target point cloud necessitating segmentation and a labeled same-type point cloud. The labeled point cloud provides preliminary labeling information, assisting in segmenting the target point cloud. A feature combination module is proposed to identify and combine corresponding features across the point clouds. The module augments the feature representation of the target cloud and improves its capacity for object discrimination. Experiments on the ShapeNetPart and S3DIS datasets demonstrate that when integrated into classical network architectures, the proposed approach can achieve improved segmentation performance over the corresponding networks, significantly in some of them.

Pose estimation of bolster spring based on projection roundness and genetic algorithm in narrow space

Precisely identifying the pose of the bolster spring is vital for its effective disengagement. Addressing the challenge of detecting various positional arrangements of the bolster spring within the constrained space of a bogie frame, a method of estimating the pose of bolster spring based on projected roundness and genetic algorithm is proposed. This approach not only reduces the computational complexity, but also improves the precision of pose estimation. Firstly, Statistical filtering and Random Sample Consensus (RANSAC) are employed for the segmentation of the external spring point cloud and for preliminary orientation estimation. Subsequently, the convergence performance of the genetic algorithm is enhanced through optimization of the selection operator, implementation of an adaptive genetic probability adjustment strategy, and refinement of the encoding method. Finally, an evaluation function based on the projected roundness of the external spring is developed and integrated with an improved adaptive genetic algorithm (IAGA) to achieve precise pose estimation of the bolster spring. Experimental results indicate that the proposed method significantly improves pose estimation accuracy compared to traditional approaches, with an additional time cost of less than 1.5 s. The method achieves a RMSE of 1.71 mm for position and 0.84° for orientation, providing an efficient and accurate pose estimation solution for engineering applications.

Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines.

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier's performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines.

Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception

Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods.

Arch dam point cloud segmentation based on deep feature learning and normal vector data optimization

Separating the dam body, spillway, and other structures from the point cloud in the dam area is an important step in dam deformation monitoring. Manual segmentation is time consuming and inaccurate. This study proposes a point cloud segmentation neural network model based on normal vector optimization suitable for dam environment: (1) This model utilizes the voxel uniform sampling method of equal length cubes to solve the problem of uneven point cloud density caused by wide range and long-distance measurement during point cloud measurement in dam areas. (2) Designed block input and combined output modules in the model, achieving efficient input of large volume point cloud and eliminating the impact of interpolation points offset during seq2seq model decoding process. (3) In response to the diverse characteristics of point cloud normal vectors presented by vegetation, rock mass, and complex dam structures in the dam area, this paper proposes an adaptive radius plane fitting vector estimation method based on eigenvalue method to improve the accuracy of segmentation. The experiment on the prototype arch dam shows that the proposed normal estimation method improves the classification accuracy of PointNet + + from 96.26 to 98.27%. Compared with the other three normal estimation methods (2-jets, Hough CNN, iterative PCA), the overall accuracy is improved by 0.82%, 1.22%, and 0.22%, and the joint average intersection is improved by 0.0293, 0.0325, and 0.0104. The prototype arch dam experiment shows that our proposed method has a segmentation accuracy of 98.27%. Compared with 2-jets, Hough CNN, and iterative PCA, the overall accuracy has been improved by 0.82%, 1.22%, and 0.22%. This study provides a high-precision segmentation scheme for applications such as deformation detection of dam components based on point clouds.

Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations

This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Maize Phenotypic Parameters Based on the Constrained Region Point Cloud Phenotyping Algorithm as a Developed Method

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R2 of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R2 of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction.

EIDU-Net: edge-preserved inception DenseGCN U-Net for LiDAR point cloud segmentation

With the development of laser scanners and machine learning, point cloud semantic segmentation plays a significant role in autonomous driving, scene reconstruction, human-computer interaction, and other fields. In recent years, point cloud semantic segmentation based on deep learning has become one of the key research directions in point cloud processing. Due to the limited ability to exploit geometric details and contextual information in point clouds, most methods that adopt encoder-decoder architecture lose local structural information easily, especially detailed features, and extract features insufficiently. To address this issue, the edge-preserving inception DenseGCN U-Net (entitled as EIDU-Net) is proposed. EIDU-Net makes full use of the complementation between geometric details in the original point cloud and high-level features. The edge-preserved graph pooling (EGP) layer, the key module of the EIDU-Net, is designed to retain additional edge feature information from the original point cloud during pooling operations. Accordingly, the edge-preserved graph unpooling (EGU) layer can restore the feature graph more efficiently based on the additionally retained edge features. Extensive experiments demonstrate that our proposed EIDU-Net has remarkable improvements on semantic segmentation tasks under whatever S3DIS or Terracotta Warrior fragments. Our code is publicly available at https://github.com/caoxin918/EIDU-Net.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Stone instance segmentation of rubble masonry based on laser scanning point clouds

Laser scanning allows for objective area-based analysis of water dam structures to enable targeted interventions in case of displacements, requiring comparison of the same areas over different epochs. This comparison improves if identical areas, e.g. stones, from multiple epochs can be automatically derived in advance. We present an instance segmentation algorithm based on a 3D point cloud with reflected intensity information to identify individual stones in rubble masonry dams that can be used within deformation monitoring. The algorithm uses initial k-means classification followed by image-based processing steps, resulting in a segmented 3D point cloud with individual stones. It is evaluated against a manually labeled reference and analyzed on terrestrial laser scanning (TLS) and UAV-based data sets. Correctly identified stones are 89.92% for TLS and 90.82% for UAV data. Additionally, stone centroids and shapes were evaluated without significant deviation. A first outlook on deformation analysis was provided using ICP matching of stones from two simulated epochs.

HBIM MODELLING PROCESS FROM 3D POINT CLOUDS BY APPLYING ARTIFICIAL INTELLIGENCE ALGORITHMS IN CULTURAL HERITAGE

In the context of Cultural Heritage (CH), the widespread adoption of 3D point cloud technology, coupled with Artificial Intelligence (AI) algorithms, plays a pivotal role. These technologies facilitate the creation of as-built models by integrating Building Information Modelling (BIM) strategies, enhancing collaboration within the Architecture, Engineering, and Construction (AEC) sector. Leveraging computer vision, robotics, and remote sensing, 3D points clouds provide rich data. However, manual segmentation and classification are labor-intensive and error prone. Consequently, researchers increasingly turn to machine learning (ML) and deep learning (DL) techniques for automating these tasks. The transition from manual reconstruction to automated procedures is crucial. Despite progress, gaps remain, particularly in incorporating 3D point cloud segmentation into Historical Building Information Modelling (HBIM). The lack of conclusive evidence regarding automated derivation of parametric attributes from segmentation outcomes underscores the need for further exploration. Addressing this gap is essential for cultural asset documentation, conservation, and upkeep. By automating the segmentation and classification of 3D point clouds, efficient communication via a shared database becomes feasible. The article aims to review studies on semantically parsing and classifying 3D point clouds using AI algorithms, particularly within complex cultural heritage geometries, shedding light on potential benefits and barriers.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Deep learning network for indoor point cloud semantic segmentation with transferability

Semantic segmentation is crucial for interpreting point cloud data and plays a fundamental role in automating the creation of as-built BIM. Existing neural network models for semantic segmentation often heavily rely on the training dataset, resulting in a significant performance drop when applied to new datasets. This paper presents AttTransNet, a neural network model for automated point cloud semantic segmentation. Its attention-based pooling module improves local feature extraction from point clouds while reducing computational costs. The transfer learning framework enhances segmentation accuracy with minimal training on target datasets. Comparative experiments show that AttTransNet reduces training time by 80 % and improves segmentation accuracy by over 20 % compared with other SOTA methods. Cross-dataset experiments reveal that the transfer learning framework increases accuracy on new datasets by 150 %. By adding semantic information to point clouds, AttTransNet aids BIM modelers with direct reference, encouraging broader application of automated point cloud segmentation in the industry.

Automatic measurement of rebar spacing based on 3D point cloud segmentation using Rebar-YOLOv8-seg and depth data

In power transmission and transformation expansion projects, construction personnel must measure rebar spacing at construction sites to ensure the quality of concrete structures. Due to electrified equipment, high-precision steel rulers are prohibited. We introduced a new method for automatically measuring rebar spacing to improve construction safety and work efficiency. We developed the Rebar-YOLOv8-seg model to accurately extract the rebar image mask from complex backgrounds. Subsequently, the rebar mask was aligned with depth data to create a point cloud, which performed statistical filtering and principal component analysis. Finally, we used the Random Sample Consensus method to fit the point cloud centerline, then extracted key points and calculated rebar spacing. Experiments on 4 × 4 rebar measurements using the proposed method showed that, within a specific range of camera heights and shooting angles, the measured values of rebar spacing meet the engineering measurement requirements. The method achieves an average absolute error of 1.98 mm and an average relative error of 1.76 %. Additionally, multiple adjacent rebar spacings can be measured simultaneously and completed within 5 s, providing a new feasible approach for rebar spacing measurement.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.