An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

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.

Large-scale 3D Semantic Mapping Using Stereo Vision

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.

Next Generation Semantic and Spatial Joint Perception——Neural Metric-Semantic Understanding

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.

Visual SLAM Based on Object Detection Network:A Review

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.

Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level

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.

An Investigation of Two Ways of Teaching Vocabulary to L2 Learners

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.

高精地图的知识图谱表达

高精地图是自动驾驶的“传感器”,为自动驾驶提供必要的先验数据以及相应的超视距感知、校验定位、动态规划和决策控制。然而,高精地图数据供给与自动驾驶知识需求仍存在鸿沟,包括数据量大导致查询困难、数据关联弱导致语义理解和智能决策困难。知识图谱是将知识以图的结构表达出来,以描述实体及其关系,涉及实体抽取和关系抽取。为此,在高精地图数据基础上,引入知识图谱,提出高精地图知识图谱的构建方法,以架起地图数据供给与驾驶知识需求之间的桥梁,支撑高精地图数据到自动驾驶知识的转化。构建的知识图谱实例,一方面将高精地图海量数据采用图进行了二次表达,建立了类似于索引的结构;另一方面显式表达了面向自动驾驶需求的语义关系。实验结果表明,知识图谱能为高精地图的语义查询、知识推理和局部决策规划提供基础。所提出的方法能实现高精地图先验数据的语义结构化,推进高精地图由数据到信息到知识的跨越,为自动驾驶的落地贡献先验知识。