An attempt of systemizing notions in the sphere of linguistic conceptology

The author discusses further evolution of linguoconceptology – a branch of science located at the junction of language and culture studies. The author notes that it is still passing the stage of formation. This is manifested in the ongoing discussions about its key notions, in insufficiently hard and fast lines between the meanings of its terms, in partial intersection of notion volumes. As to the level of systemness, linguoconceptology is still inferior to lexical semantics, which in the 20th Сentury became a highly systemized discipline under the influence of structuralism. In this regard, the author set the task to clarify the ratio of the volumes and contents of some related notions of linguoconceptology, to reveal the nature of the connections between them, to eliminate terminological duplication, to avoid intersection of term meanings and, in general, to sharply outline the semantic grid that linguoconceptology imposes on its object. In particular, the author describes the types of relations between members of the conceptual field (incorporation, hyper-hyponymy, centering), offers an interpretation of the relationship between the notions of “concept zone”, “conceptual field” and “conceptual sphere”, discusses the possibility of using the allo-emic approach in linguoconceptology.

Semantic Evidential Grid Mapping Using Monocular and Stereo Cameras

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity, it is desired to add vision-based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline, including estimates for eight semantic classes, that is designed for straightforward fusion with range sensor data. Unlike other publications, our representation explicitly models uncertainties in the evidential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping results are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the KITTI odometry benchmark dataset and demonstrating the advantages compared to other semantic grid mapping approaches.

Semantic Evidential Grid Mapping Using Monocular and Stereo Cameras

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants, information on the semantics may also be included in the desired representation. Multi-layer grid maps allow to include all this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as LIDAR and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity it is desired to add vision based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline including estimates for eight semantic classes that is designed for straightforward fusion with range sensor data. Unlike in other publication our representation explicitly models uncertainties in the evdiential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping resulsts are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the Kitti odometry benchmark and demonstrating the advantages compared to other semantic grid mapping approaches.

Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map

For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments.

Homonymous and tautological rhymes in K. Simonov’s poetry

The article explores homonymous and tautological rhymes in K. Simonovs poetry as a form of language game. The study of rhymes in the authors poetry, as well as the appeal to the categories of play-element poetics in connection with his work, is realized for the first time. The term polysemantic rhyme is introduced to denote the transitional case between the two types of rhymes which is often found in Simonovs poetry. The dynamics of rhyme preferences and their functions throughout Simonovs work is revealed. In the early poems, he actively uses homonymous rhymes, that have a characterizing and world-modeling function. Rhyme becomes part of a more complex game and is reinforced by repetitions of similar-root and similar-sounding words, that create a dense semantic grid of important to Simonov concepts. In mature creativity, the poet often turns to polysemantic rhyme, and if he puts homonyms at the end of the line, they does not always rhyme with each other. While maintaining the previous functions, traditional humorous and compositional ones are added to them, the latter is associated with the separation of lines as independent aphorisms.

Semantic - based service discovery in grid environment

In the process of informatization, there are also some new problems, mainly information can’t be shared and integrated, distributed resources can’t be used effectively, these problems make the industry face new challenges. The goal of this paper is to combine the grid technology and ontology organically, to build a unified information system integration and interoperation platform based on semantics, to realize information sharing and accelerate the pace of informatization. The method is to construct the whole structure of the system according to the actual needs of the system. This paper firstly analyzes the current research status and existing problems of semantic grid service matching, and proposes a semantic layered matching algorithm based on Massimo Paolucci elastic matching algorithm. To verify the feasibility and effectiveness of the hierarchical matching algorithm based on semantics, a prototype system named SGSM was designed and its functional model, matching process and performance were studied. Experimental results show that for the semantic-based hierarchical matching algorithm proposed in this paper, the threshold value of service semantic correlation degree is 0.84, the threshold value of service basic concept matching degree is 0.89, the threshold value of service comprehensive similarity degree is 0.66, and the threshold value of service quality matching degree is 0.78. Statistics through the experiment, the above three methods of recall, respectively, 33%, 62%, 85%, the precision is respectively: 29%, 57%, 88%, and illustrate the hierarchical matching algorithm based on semantic is feasible in practical application, compared with the traditional service based on keyword matching algorithm and Massimo Paolucci elastic matching algorithm on the recall and precision are improved significantly.

Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation

Occupied grid maps are sufficient for mobile robots to complete metric navigation tasks in domestic environments. However, they lack semantic information to endow the robots with the ability of social goal selection and human-friendly operation modes. In this paper, we propose an object semantic grid mapping system with 2D Light Detection and Ranging (LiDAR) and RGB-D sensors to solve this problem. At first, we use a laser-based Simultaneous Localization and Mapping (SLAM) to generate an occupied grid map and obtain a robot trajectory. Then, we employ object detection to get an object’s semantics of color images and use joint interpolation to refine camera poses. Based on object detection, depth images, and interpolated poses, we build a point cloud with object instances. To generate object-oriented minimum bounding rectangles, we propose a method for extracting the dominant directions of the room. Furthermore, we build object goal spaces to help the robots select navigation goals conveniently and socially. We have used the Robot@Home dataset to verify the system; the verification results show that our system is effective.

Integrity Monitoring of Multimodal Perception System for Vehicle Localization

Autonomous driving systems tightly rely on the quality of the data from sensors for tasks such as localization and navigation. In this work, we present an integrity monitoring framework that can assess the quality of multimodal data from exteroceptive sensors. The proposed multisource coherence-based integrity assessment framework is capable of handling highway as well as complex semi-urban and urban scenarios. To achieve such generalization and scalability, we employ a semantic-grid data representation, which can efficiently represent the surroundings of the vehicle. The proposed method is used to evaluate the integrity of sources in several scenarios, and the integrity markers generated are used for identifying and quantifying unreliable data. A particular focus is given to real-world complex scenarios obtained from publicly available datasets where integrity localization requirements are of high importance. Those scenarios are examined to evaluate the performance of the framework and to provide proof-of-concept. We also establish the importance of the proposed integrity assessment framework in context-based localization applications for autonomous vehicles. The proposed method applies the integrity assessment concepts in the field of aviation to ground vehicles and provides the Protection Level markers (Horizontal, Lateral, Longitudinal) for perception systems used for vehicle localization.

Building semantic grid maps for domestic robot navigation

This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.