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3d semantic map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment

IEEE ACCESS 2020年 8卷 67179-67191页

作者： Cui, Xu Lu, Chenggang Wang, Jinxiang Yanbian Univ Dept Comp Sci & Technol Yanji 133002 Peoples R China

Although the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3d semantic map construction of mobile robot based on improved ORB-SALM2 is proposed. Firstly, the improved yolov3 algorithm is used to detect indoor objects, and then the real-time semantic segmentation network model based on deep learning is used to segment indoor objects to achieve the classification of pixel points of objects on two-dimensional images, and BAFF feature fusion algorithm is introduced to improve the accuracy of semantic segmentation model. Then, through the SLAM system, we estimate the pose of the image in the result of semantic segmentation, and use the depth information to project it into the three-dimensional environment to build the three-dimensional semantic map. Finally, the experiment platform of mobile robot is built to verify the stability of ORB-d and thermal imaging sensor registration technology, the accuracy and real-time of building three-dimensional environment thermal field map, and the accuracy of robot positioning using thermal infrared and depth image.

关键词： Mobile robot edge computing 3d semantic map improved ORB-SALM2 pose estimation image semantic segmentation deep learning

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3d semantic map Construction System Based on Visual SLAM and CNNs 46

3D Semantic Map Construction System Based on Visual SLAM and...

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46th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Lai, Lei Yu, Xinyi Qian, Xuecheng Ou, Linlin Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China

ISBN: (纸本)9781728154145

Traditional approaches to simultaneous localization and mapping (SLAM) are unable to extract semantic information from the scene or meet the high-level task requirements of robots, and the efficiency of 3d map construction is low. To solve this problem, a 3d semantic map construction system is proposed in the paper to build a 3d semantic map. Firstly, the current position of the camera is estimated and optimized based on ORB-SLAM algorithm, and the globally consistent trajectory and pose are obtained. Then the semantic segmentation network is designed to predict semantic category of every pixel. The 3d semantic point cloud information is generated by combing the semantic information and the object point cloud. The global consistent camera pose estimated by visual SLAM algorithm is integrated with the semantic point cloud information to generate a 3d semantic map. Finally, we use an Octomap which is applied to navigation projects for map storage to reduce the storage capacity of the map. The experimental result verifies the accuracy and efficiency of this method.

关键词： visual slam semantic segmentation semantic fusion 3d semantic map

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development of Autonomous Navigation System Using 3d map with Geometric and semantic Information

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JOURNAL OF ROBOTICS ANd MECHATRONICS 2017年第4期29卷 639-648页

作者： Aotani, Yoshihiro Ienaga, Takashi Machinaka, Noriaki Sadakuni, Yudai Yamazaki, Ryota Hosoda, Yuki Sawahashi, Ryota Kuroda, Yoji Meiji Univ Dept Mech Engn Sch Sci & Technol Tama Ku 1-1-1 Higashimita Kawasaki Kanagawa 2148571 Japan

This paper presents an autonomous navigation system. Our system is based on an accurate 3d map, which includes "geometric information" (e.g., curb, wall, street tree) and "semantic information" (e.g., sidewalk, roadway, crosswalk) extracted by environmental recognition. By using the semantic map, we can obtain the suitable area to keep away from undesired places. Furthermore, by comparing the map with real-time 3d geometric information from LI-dAR, we obtain the robot position. To show the effectiveness of our system, we conduct a 3d semantic map construction experiment and driving test. The experiment results show that the proposed system enables accurate and highly reproducible localization and stable autonomous mobility.

关键词： autonomous navigation robot 3d semantic map localization

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An Improved High Precision 3d semantic mapping of Indoor Scenes from RGB-d Images

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Computer Modeling in Engineering & Sciences 2023年第12期137卷 2621-2640页

作者： Jing Xin Kenan du Jiale Feng Mao Shan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an710048China Australian Centre for Field Robotics The University of SydneySydney2006Australia

This paper proposes an improved high-precision 3d semantic mapping method for indoor scenes using RGB-d *** current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time *** address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space ***,an indoor RGB-d image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud ***,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud *** also employ dense conditional random fields(CRF)to optimize the 3d semantic map model,resulting in a high-precision spatial semantic map of indoor *** results show that the proposed semantic mapping system can process image sequences collected by RGB-d sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic ***,it constructs a globally consistent high-precision indoor scenes 3d semantic map.

关键词： 3d semantic map online reconstruction RGB-d images semantic segmentation indoor mobile robot

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Monocular semantic mapping Based on 3d Cuboids Tracking 53

Monocular Semantic Mapping Based on 3D Cuboids Tracking

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Ji, Xingwu Gong, Zheng Miao, Ruihang Xue, Wuyang Ying, Rendong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728192017

semantic mapping based on information of objects has become a crucial component for the surrounding comprehension and the more robust navigation. In this paper, we propose a system for simultaneous localization and mapping (SLAM) that combines multiple objects tracking and factor graph optimization with semantically meaningful landmarks to achieve accurate monocular semantic mapping. Firstly, the process of object recognition uses a vanishing point sampling-based approach to efficiently infer the class and position of object landmarks from 2d bounding box object detection. Secondly, The semantic frontend utilizes local matching-based data association to track raw cuboid proposals. It can provide semantic constraints to reduce cuboid scale drift and improve its position estimation. Finally, we present a multi-view factor graph optimization which can use motion modal of the camera to optimize stable cuboids. The semantic mapping experiments on our own built virtual scene show better accuracy and robustness over existing approaches. We evaluate the effectiveness of our approach on public KITTI datasets and a real scene.

关键词： visual simultaneous localization and mapping (SLAM) 3d semantic map factor graph optimization

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Reconstruction of High-Precision semantic map

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SENSORS 2020年第21期20卷 6264.-6264.页

作者： Tu, Xinyuan Zhang, Jian Luo, Runhao Wang, Kai Zeng, Qingji Zhou, Yu Yu, Yao du, Sidan Nanjing Univ Sch Elect Sci & Engineer Nanjing 21000 Peoples R China

We present a real-time Truncated Signed distance Field (TSdF)-based three-dimensional (3d) semantic reconstruction for LidAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3d semantic reconstruction in real time on LidAR data is important but challenging. Lighting detection and Ranging (LidAR) data with high accuracy is massive for 3d reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

关键词： 3d semantic map 3d reconstruction semantic fusion

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semantic Scene mapping with Spatio-temporal deep Neural Network for Robotic Applications

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COGNITIVE COMPUTATION 2018年第2期10卷 260-271页

作者： Li, Ruihao Gu, dongbing Liu, Qiang Long, Zhiqiang Hu, Huosheng Univ Essex Dept Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Natl Univ Def Technol Coll Mechatron & Automat Changsha Hunan Peoples R China

semantic scene mapping is a challenge and significant task for robotic application, such as autonomous navigation and robot-environment interaction. In this paper, we propose a semantic pixel-wise mapping system for potential robotic applications. The system includes a novel spatio-temporal deep neural network for semantic segmentation and a Simultaneous Localisation and mapping (SLAM) algorithm for 3d point cloud map. Their combination yields a 3d semantic pixel-wise map. The proposed network consists of Convolutional Neural Networks (CNNs) with two streams: spatial stream with images as the input and temporal stream with image differences as the input. due to the use of both spatial and temporal information, it is called spatio-temporal deep neural network, which shows a better performance in both accuracy and robustness in semantic segmentation. Further, only keyframes are selected for semantic segmentation in order to reduce the computational burden for video streams and improve the real-time performance. Based on the result of semantic segmentation, a 3d semantic map is built up by using the 3d point cloud map from a SLAM algorithm. The proposed spatio-temporal neural network is evaluated on both Cityscapes benchmark (a public dataset) and Essex Indoor benchmark (a dataset we labelled ourselves manually). Compared with the state-of-the-art spatial only neural networks, the proposed network achieves better performances in both pixel-wise accuracy and Intersection over Union (IoU) for scene segmentation. The constructed 3d semantic map with our methods is accurate and meaningful for robotic applications.

关键词： deep learning Spatio-temporal neural network 3d semantic map Robotics

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Indoor Instance-Aware semantic mapping Using Instance Segmentation 33

Indoor Instance-Aware Semantic Mapping Using Instance Segmen...

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33rd Chinese Control and decision Conference (CCdC)

作者： Jiang, Yinpeng Ma, Xudong Fang, Fang Kang, Xuewen Southeast Univ Acad Automat Nanjing 210000 Peoples R China Huaibei Normal Univ Huaibei 235000 Peoples R China

ISBN: (纸本)9781665440899

In order to accomplish the requirement of scene understanding to complete various kinds of complex tasks in home environment for robots, a novel instance segmentation method is adopted to build an instance-level 3d semantic map and obtain information such as categories, positions and interrelationship of instance objects within the environment. different from the previous method which focuses on a certain feature in geometry or vision, we synchronously learn the features of geometric and visual information, distinguish instance objects and background areas and create the feature voxel grid of the environment. The proposed 3d-RPN network takes the grid as input and makes use of the cuboid bounding box to predict each instance and the category it represents. With the mask prediction branch, we binarized voxels in each bounding box to determine the exact distribution of the instance object. Our method borrows the idea of Mask R-CNN and the main body is constructed by 3d and 2d convolutional network, making full use of the features of 2d and 3d. We have tested our method on ScanNet and S3dIS, two large-scale indoor scene data sets, and the experiment has verified that our method can find and identify the instance information more accurately than previous methods.

关键词： 3d semantic map Instance Segmentation Feature Voxel Grid 3d-RPN Mask Feature

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Real-world Instance-specific Image Goal Navigation: Bridging domain Gaps via Contrastive Learning 8

Real-world Instance-specific Image Goal Navigation: Bridging...

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8th International Conference on Robotic Computing

作者： Sakaguchi, Taichi Taniguchi, Akira Hagiwara, Yoshinobu El Hafi, Lotfi Hasegawa, Shoichi Taniguchi, Tadahiro Ritsumeikan Univ Grad Sch Info Sci & Engn Osaka Japan Ritsumeikan Univ Coll Info Sci & Engn Osaka Japan Soka Univ Fac Sci & Engn Tokyo Japan Ritsumeikan Univ Res Org Sci & Tech Kusatsu Shiga Japan Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9798331521561;9798331521554

Improving instance-specific image goal navigation (InstanceImageNav), which involves locating an object in the real world that is identical to a query image, is essential for enabling robots to help users find desired objects. The challenge lies in the domain gap between the low-quality images observed by the moving robot, characterized by motion blur and low resolution, and the high-quality query images provided by the user. These domain gaps can significantly reduce the task success rate, yet previous work has not adequately addressed them. To tackle this issue, we propose a novel method: few-shot cross-quality instance-aware adaptation (CrossIA). This approach employs contrastive learning with an instance classifier to align features between a large set of low-quality images and a small set of high-quality images. We fine-tuned the SimSiam model, pre-trained on ImageNet, using CrossIA with instance labels based on a 3d semantic map. Additionally, our system integrates object image collection with a pre-trained deblurring model to enhance the quality of the observed images. Evaluated on an InstanceImageNav task with 20 different instance types, our method improved the task success rate by up to three-fold compared to a baseline based on SuperGlue. These findings highlight the potential of contrastive learning and image enhancement techniques in improving object localization in robotic applications. The project website is https://***/domainBridgingNav/.

关键词： contrastive learning few-shot domain adaptation instance-specific image goal navigation 3d semantic map

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YES-SLAM: YOLOv7-enhanced-semantic visual SLAM for mobile robots in dynamic scenes

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MEASUREMENT SCIENCE ANd TECHNOLOGY 2024年第3期35卷 035117-035117页

作者： Liu, Hang Luo, Jingwen Yunnan Normal Univ Sch Informat Sci & Technol 768 Juxian St Kunming 650500 Yunnan Peoples R China Engn Res Ctr Comp Vis & Intelligent Control Techno Dept Educ Yunnan Prov Kunming Yunnan Peoples R China

In dynamic scenes, moving objects will cause a significant error accumulation in robot's pose estimation, and might even lead to tracking loss. In view of these problems, this paper proposes a semantic visual simultaneous localization and mapping algorithm based on YOLOv7. First, a light-weight network YOLOv7 is employed to acquire the semantic information of different objects in the scene, and flood filling and edge-enhanced techniques are combined to accurately and quickly separate the dynamic feature points from the extracted feature point set. In this way, the obtained static feature points with high-confidence are used to achieve the accurate estimation of robot's pose. Then, according to the semantic information of YOLOv7, the motion magnitude of the robot, and the number of dynamic feature points in camera's field-of-view, a high-performance keyframe selection strategy is constructed. On this basis, a robust loop closure detection method is developed by introducing the semantic information into the bag-of-words model, and global bundle adjustment optimization is performed on all keyframes and map points to obtain a global consistent pose graph. Finally, YOLOv7 is further utilized to carry out semantic segmentation on the keyframes, remove the dynamic objects in its semantic mask, and combine the point cloud pre-processing and octree map to build a 3d navigation semantic map. A series of simulations on TUM dataset and a case study in real scene clearly demonstrated the performance superiority of the proposed algorithms.

关键词： dynamic scenes simultaneous localization and mapping (SLAM) YOLOv7 depth camera loop closure detection 3d semantic map

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