scholarly journals Semantic Mapping for Mobile Robots in Indoor Scenes: A Survey

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 92
Author(s):  
Xiaoning Han ◽  
Shuailong Li ◽  
Xiaohui Wang ◽  
Weijia Zhou
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Semantic Mapping ◽  
Human Beings ◽  
Future Directions ◽  
Essential Requirement ◽  
Semantic Map ◽  
Semantic Maps ◽  
Great Progress ◽  
Indoor Scenes

Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

Robust Semantic Mapping in Challenging Environments

Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 256-270 ◽  
Author(s):  
Jiyu Cheng ◽  
Yuxiang Sun ◽  
Max Q.-H. Meng
Keyword(s):  
Semantic Information ◽  
Moving Objects ◽  
Dynamic Environments ◽  
Semantic Mapping ◽  
Visual Slam ◽  
The Public ◽  
Semantic Map ◽  
Recent Success ◽  
3D Space ◽  
Localization And Mapping

SummaryVisual simultaneous localization and mapping (visual SLAM) has been well developed in recent decades. To facilitate tasks such as path planning and exploration, traditional visual SLAM systems usually provide mobile robots with the geometric map, which overlooks the semantic information. To address this problem, inspired by the recent success of the deep neural network, we combine it with the visual SLAM system to conduct semantic mapping. Both the geometric and semantic information will be projected into the 3D space for generating a 3D semantic map. We also use an optical-flow-based method to deal with the moving objects such that our method is capable of working robustly in dynamic environments. We have performed our experiments in the public TUM dataset and our recorded office dataset. Experimental results demonstrate the feasibility and impressive performance of the proposed method.

scholarly journals The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1883
Author(s):  
Jingyu Li ◽  
Rongfen Zhang ◽  
Yuhong Liu ◽  
Zaiteng Zhang ◽  
Runze Fan ◽  
...  
Keyword(s):  
Semantic Information ◽  
Semantic Map ◽  
Average Value ◽  
Map Construction ◽  
Dense Optical Flow ◽  
Semantic Maps ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
High Level ◽  
Static Semantic

Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.

Improving Dense Mapping for Mobile Robots in Dynamic Environments Based on Semantic Information

2020 ◽  
pp. 1-1
Author(s):  
Jiyu Cheng ◽  
Chaoqun Wang ◽  
Xiaochun Mai ◽  
Zhe Min ◽  
Max Q.-H. Meng
Keyword(s):  
Mobile Robots ◽  
Semantic Information ◽  
Dynamic Environments

scholarly journals Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wei Li ◽  
Junhua Gu ◽  
Benwen Chen ◽  
Jungong Han
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
3D Objects ◽  
Integration Strategy ◽  
Semantic Maps ◽  
Multiview Images ◽  
Indoor Scenes ◽  
Novel Method ◽  
The Individual

Scene parsing plays a crucial role when accomplishing human-robot interaction tasks. As the “eye” of the robot, RGB-D camera is one of the most important components for collecting multiview images to construct instance-oriented 3D environment semantic maps, especially in unknown indoor scenes. Although there are plenty of studies developing accurate object-level mapping systems with different types of cameras, these methods either process the instance segmentation problem in completed mapping or suffer from a critical real-time issue due to heavy computation processing required. In this paper, we propose a novel method to incrementally build instance-oriented 3D semantic maps directly from images acquired by the RGB-D camera. To ensure an efficient reconstruction of 3D objects with semantic and instance IDs, the input RGB images are operated by a real-time deep-learned object detector. To obtain accurate point cloud cluster, we adopt the Gaussian mixture model as an optimizer after processing 2D to 3D projection. Next, we present a data association strategy to update class probabilities across the frames. Finally, a map integration strategy fuses information about their 3D shapes, locations, and instance IDs in a faster way. We evaluate our system on different indoor scenes including offices, bedrooms, and living rooms from the SceneNN dataset, and the results show that our method not only builds the instance-oriented semantic map efficiently but also enhances the accuracy of the individual instance in the scene.

scholarly journals SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation

2019 ◽  
Vol 128 (5) ◽  
pp. 1286-1310 ◽  
Author(s):  
Oscar Mendez ◽  
Simon Hadfield ◽  
Nicolas Pugeault ◽  
Richard Bowden
Keyword(s):  
Deep Learning ◽  
Semantic Information ◽  
State Of The Art ◽  
Depth Information ◽  
Semantic Maps ◽  
Novel Method ◽  
Rgb Images ◽  
High Level ◽  
Robotic Tasks ◽  
And Robotics

Abstract The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has been enabled by advances in Deep Learning that allow consistent and robust semantic understanding. Leveraging this semantic vision of the world has allowed human-level understanding to naturally emerge from many different approaches. Particularly, the use of semantic information to aid in localisation and reconstruction has been at the forefront of both fields. Like robots, humans also require the ability to localise within a structure. To aid this, humans have designed high-level semantic maps of our structures called floorplans. We are extremely good at localising in them, even with limited access to the depth information used by robots. This is because we focus on the distribution of semantic elements, rather than geometric ones. Evidence of this is that humans are normally able to localise in a floorplan that has not been scaled properly. In order to grant this ability to robots, it is necessary to use localisation approaches that leverage the same semantic information humans use. In this paper, we present a novel method for semantically enabled global localisation. Our approach relies on the semantic labels present in the floorplan. Deep Learning is leveraged to extract semantic labels from RGB images, which are compared to the floorplan for localisation. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

scholarly journals SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jun Chen ◽  
Shize Guo ◽  
Xin Ma ◽  
Haiying Li ◽  
Jinhong Guo ◽  
...  
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Malicious Code ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Execution Sequence ◽  
Window Method ◽  
Great Progress ◽  
Sliding Window Method

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.

scholarly journals Psychologists at the Gate: A Review of Daniel Kahneman's Thinking, Fast and Slow

2012 ◽  
Vol 50 (4) ◽  
pp. 1080-1091 ◽  
Author(s):  
Andrei Shleifer
Keyword(s):  
Decision Theory ◽  
Economic Models ◽  
Human Beings ◽  
Future Directions ◽  
Economic Applications ◽  
The Past ◽  
Broad Theme ◽  
Breaking Work

The publication of Daniel Kahneman's book, Thinking, Fast and Slow, is a major intellectual event. The book summarizes, but also integrates, the research that Kahneman has done over the past forty years, beginning with his path-breaking work with the late Amos Tversky. The broad theme of this research is that human beings are intuitive thinkers and that human intuition is imperfect, with the result that judgments and choices often deviate substantially from the predictions of normative statistical and economic models. In this review, I discuss some broad ideas and themes of the book, describe some economic applications, and suggest future directions for research that the book points to, especially in decision theory. (JEL A12, D03, D80, D87)

scholarly journals Lexical diachronic semantic maps

2021 ◽  
Vol 11 (3) ◽  
pp. 367-420 ◽  
Author(s):  
Thanasis Georgakopoulos ◽  
Stéphane Polis
Keyword(s):  
Large Scale ◽  
Historical Linguistics ◽  
Qualitative Evaluation ◽  
Ancient Greek ◽  
Semantic Map ◽  
Semantic Extension ◽  
Ancient Egyptian ◽  
Semantic Maps ◽  
Scope Of Application

Abstract This paper extends the scope of application of the semantic map model to diachronic lexical semantics. Combining a quantitative approach to large-scale synchronic polysemy data with a qualitative evaluation of the diachronic material in two text languages, ancient Egyptian and ancient Greek, it shows that weighted diachronic semantic maps can capture informative generalizations about the organization of the lexicon and its reshaping over time. The general methodology developed in the paper is illustrated with a case study of the semantic extension of time-related lexemes. This case study shows that the blend of tools well established in linguistic typology with proven methods of historical linguistics enables a principled approach to long-standing questions in the fields of diachronic semasiology and onomasiology.

scholarly journals An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jianjun Ni ◽  
Tao Gong ◽  
Yafei Gu ◽  
Jinxiu Zhu ◽  
Xinnan Fan
Keyword(s):  
Semantic Information ◽  
Hot Spot ◽  
Simultaneous Localization And Mapping ◽  
Mapping Method ◽  
Monocular Vision ◽  
Visual Slam ◽  
Camera Tracking ◽  
Residual Network ◽  
Semantic Map ◽  
Localization And Mapping

The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

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