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An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images
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作者 Jing Xin Kenan Du +1 位作者 Jiale Feng Mao Shan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第12期2621-2640,共20页
This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real... This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To 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 model.Then,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 model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental 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 map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map. 展开更多
关键词 3D semantic map online reconstruction RGB-D images semantic segmentation indoor mobile robot
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CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations 被引量:1
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作者 刘扬 Zheng Fengbin Zuo Xianyu 《High Technology Letters》 EI CAS 2016年第1期90-98,共9页
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of co... Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure. 展开更多
关键词 multimedia neural cognitive computing (MNCC) brain-like computing cross-modal semantic mapping (CSM) selective attention limbic system multisensory integration memory-enhancing mechanism
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Building of cognizing semantic map in large-scale semi-unknown environment
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作者 吴皓 田国会 +2 位作者 李岩 桑森 张海婷 《Journal of Central South University》 SCIE EI CAS 2014年第5期1804-1815,共12页
The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only... The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably. 展开更多
关键词 artificial label distributed information representation cognizing semantic map service robot
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Next Generation Semantic and Spatial Joint Perception——Neural Metric-Semantic Understanding
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作者 ZHU Fang 《ZTE Communications》 2021年第1期61-71,共11页
Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dens... Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario. 展开更多
关键词 visual computing semantic and spatial joint perception dense 3D semantic map⁃ping neural metric-semantic understanding
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Visual SLAM Based on Object Detection Network:A Review
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作者 Jiansheng Peng Dunhua Chen +3 位作者 Qing Yang Chengjun Yang Yong Xu Yong Qin 《Computers, Materials & Continua》 SCIE EI 2023年第12期3209-3236,共28页
Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous driving.However,traditional visual SLAM faces challenges in dynamic environments.To address this issue,researchers have proposed ... Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous driving.However,traditional visual SLAM faces challenges in dynamic environments.To address this issue,researchers have proposed semantic SLAM,which combines object detection,semantic segmentation,instance segmentation,and visual SLAM.Despite the growing body of literature on semantic SLAM,there is currently a lack of comprehensive research on the integration of object detection and visual SLAM.Therefore,this study aims to gather information from multiple databases and review relevant literature using specific keywords.It focuses on visual SLAM based on object detection,covering different aspects.Firstly,it discusses the current research status and challenges in this field,highlighting methods for incorporating semantic information from object detection networks into mileage measurement,closed-loop detection,and map construction.It also compares the characteristics and performance of various visual SLAM object detection algorithms.Lastly,it provides an outlook on future research directions and emerging trends in visual SLAM.Research has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal,data association,point cloud segmentation,and other technologies.It can improve the robustness and accuracy of the entire SLAM system and can run in real time.With the continuous optimization of algorithms and the improvement of hardware level,object visual SLAM has great potential for development. 展开更多
关键词 Object detection visual SLAM visual odometry loop closure detection semantic map
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An Investigation of Two Ways of Teaching Vocabulary to L2 Learners
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作者 詹玉月 《海外英语》 2017年第24期251-252,共2页
As for language learners, vocabulary acquisition is one of the most important tasks, and the same goes for L2 teachers.Recently, some researchers claim that semantic mapping is far more effective in vocabulary. The pr... As for language learners, vocabulary acquisition is one of the most important tasks, and the same goes for L2 teachers.Recently, some researchers claim that semantic mapping is far more effective in vocabulary. The present study comparatively investigate two strategies of teaching vocabulary to non-English lower-intermediate learners. One is the traditional grammatical description and drilling way, the other is the semantic mapping. Six new words were taught by using these two ways respectively,and a sample test was applied to check students' mastery of these words at the end of the class. The test results show that there is no significant difference between the learning outcomes of these two different teaching approaches, but learners turned out to be more engaged when the teacher conducted the semantic links teaching method. Therefore, perhaps, a combination of these two approaches would be a good choice for those L2 learners. 展开更多
关键词 vocabulary acquisition L2 learner grammatical descriptions and drilling semantic mapping EFL classroom
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Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level 被引量:1
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作者 Benny Wijaya Kun Jiang +3 位作者 Mengmeng Yang Tuopu Wen Xuewei Tang Diange Yang 《Automotive Innovation》 EI CSCD 2022年第1期43-56,共14页
High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on ... High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition. 展开更多
关键词 Crowdsourced mapping Map fusion SLAM semantic mapping
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Large-scale 3D Semantic Mapping Using Stereo Vision
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作者 Yi Yang Fan Qiu +3 位作者 Hao Li Lu Zhang Mei-Ling Wang Meng-Yin Fu 《International Journal of Automation and computing》 EI CSCD 2018年第2期194-206,共13页
In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping (SLAM) systems. This paper presents an approach to generate an outdoor large-scale 3D dense s... In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping (SLAM) systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field (CRF) inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective. 展开更多
关键词 semantic map stereo vision motion segmentation visual odometry simultaneous localization and mapping (SLAM).
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