scholarly journals Topological Frontier-Based Exploration and Map-Building Using Semantic Information

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4595 ◽  
Author(s):  
Clara Gomez ◽  
Alejandra C. Hernandez ◽  
Ramon Barber
Keyword(s):  
Open Space ◽  
Semantic Information ◽  
State Of The Art ◽  
Fundamental Problem ◽  
Cost Utility ◽  
Indoor Environments ◽  
Map Building ◽  
Topological Map ◽  
Unknown Area ◽  
Closure Algorithm

Exploration of unknown environments is a fundamental problem in autonomous robotics that deals with the complexity of autonomously traversing an unknown area while acquiring the most important information of the environment. In this work, a mobile robot exploration algorithm for indoor environments is proposed. It combines frontier-based concepts with behavior-based strategies in order to build a topological representation of the environment. Frontier-based approaches assume that, to gain the most information of an environment, the robot has to move to the regions on the boundary between open space and unexplored space. The novelty of this work is in the semantic frontier classification and frontier selection according to a cost–utility function. In addition, a probabilistic loop closure algorithm is proposed to solve cyclic situations. The system outputs a topological map of the free areas of the environment for further navigation. Finally, simulated and real-world experiments have been carried out, their results and the comparison to other state-of-the-art algorithms show the feasibility of the exploration algorithm proposed and the improvement that it offers with regards to execution time and travelled distance.

scholarly journals Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Francisco Amorós ◽  
Luis Payá ◽  
Oscar Reinoso ◽  
Walterio Mayol-Cuevas ◽  
Andrew Calway
Keyword(s):  
Pose Estimation ◽  
Visual Information ◽  
Multiscale Analysis ◽  
Spatial Arrangement ◽  
Field Of View ◽  
Indoor Environments ◽  
Map Building ◽  
Topological Map ◽  
Complete Field ◽  
Estimation System

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.

scholarly journals MULTI-LEVEL INDOOR PATH PLANNING METHOD

2015 ◽  
Vol XL-4/W5 ◽  
pp. 19-23 ◽  
Author(s):  
Q. Xiong ◽  
Q. Zhu ◽  
S. Zlatanova ◽  
Z. Du ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Semantic Information ◽  
Fundamental Problem ◽  
Relevant Literature ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
3D Cad ◽  
Building Models ◽  
Complex Building ◽  
Building Components

Indoor navigation is increasingly widespread in complex indoor environments, and indoor path planning is the most important part of indoor navigation. Path planning generally refers to finding the most suitable path connecting two locations, while avoiding collision with obstacles. However, it is a fundamental problem, especially for 3D complex building model. A common way to solve the issue in some applications has been approached in a number of relevant literature, which primarily operates on 2D drawings or building layouts, possibly with few attached attributes for obstacles. Although several digital building models in the format of 3D CAD have been used for path planning, they usually contain only geometric information while losing abundant semantic information of building components (e.g. types and attributes of building components and their simple relationships). Therefore, it becomes important to develop a reliable method that can enhance application of path planning by combining both geometric and semantic information of building components. This paper introduces a method that support 3D indoor path planning with semantic information.

Topological Map Building Based on Thinning

2014 ◽  
Vol 668-669 ◽  
pp. 384-387
Author(s):  
Na Wang ◽  
Hai Yan Wang ◽  
Qing Qing Zhu
Keyword(s):  
Path Planning ◽  
Fundamental Problem ◽  
Map Building ◽  
Robotic Control ◽  
Topological Map ◽  
Topological Information ◽  
Key Technology ◽  
Thinning Algorithm ◽  
Grid Map ◽  
Autonomous Operation

In order to accomplish navigation and path planning, map building is the key technology and fundamental problem in mobile robotic control in unknown environment. In this paper the topological map is built based on the grid map by using a thinning algorithm. The environment is modeled in grid map, next the thinning algorithm is applied in the map, and then the effective topological information is extracted. Simulation results show that the topological map based on thinning is clear and succinct, and it also avoids producing unnecessary nodes and paths. This topological map stores much less information which will improve the ability of autonomous operation, navigation and path planning further.

Semantic Enrichment of Location-Based Services

2007 ◽  
pp. 856-862 ◽  
Author(s):  
Vassileios Tsetsos ◽  
Christos Anagnostopoulos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Semantic Web ◽  
Path Length ◽  
Semantic Information ◽  
State Of The Art ◽  
Location Based Services ◽  
Indoor Environments ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Navigation Service ◽  
Minimizing Path

In this article, we describe issues related to the development of intelligent and human-centered LBS for indoor environments. We focus on the navigation service. Navigation is probably the most challenging LBS since it involves relatively complex algorithms and many cognitive processes (e.g., combining known paths for reaching unknown destinations, minimizing path length). With the proposed system, we try to incorporate intelligence to navigation services by enriching them with the semantics of users and navigation spaces. Such semantic information is represented and reasoned using state-of-the-art semantic Web technologies (Berners-Lee, Hendler, & Lassila, 2001).

Recognition of Indoor Environment by Robot Partner Using Conversation

2013 ◽  
Vol 17 (5) ◽  
pp. 753-760 ◽  
Author(s):  
Jinseok Woo ◽  
◽  
Naoyuki Kubota
Keyword(s):  
Indoor Environment ◽  
Daily Life ◽  
Indoor Environments ◽  
Map Building ◽  
Human Beings ◽  
Topological Map ◽  
Symbolic Information ◽  
Localization And Mapping ◽  
Environment Recognition ◽  
Environment Perception

To support daily life before performing an action, a robot partner must perceive an unknown environment. Much research has been done from various viewpoints on self-localization estimation and environment perception. In our research, the robot partner performs self-localization and environment recognition using Simultaneous Localization and Mapping for self-localization estimation and map building. In this paper, we propose a method for recognizing indoor environments by robot partners based on conversations with human beings. Information acquired from maps is identified in order to share the meaning with human beings after the required interpretation. In this paper, we therefore propose a method for recognizing environmental maps by labeling these maps based on symbolic information developed through conversation with human beings. The proposed method is composed of four parts. First, the robot partner applies a steady-state genetic algorithm for self-localization estimation. Second, we use a map building algorithm for expressing the topological map. Third, conversation with human beings is performed for acquiring symbolic information in order to recognize object and position locations through the map. Fourth, we perform experiments and discuss the effectiveness of the proposed technique.

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

scholarly journals Smart Skin Envelope

2011 ◽  
Author(s):  
Rosa Romano
Keyword(s):  
State Of The Art ◽  
Innovation Process ◽  
Research Programme ◽  
Energy Performance ◽  
Indoor Environments ◽  
Performance Parameters ◽  
Skin Envelope ◽  
And Performance ◽  
Definition Of ◽  
Smart Skin

The Smart Skin Envelope research analyses the recent revolution that has taken place in the sector of planning and production of smart skin components, made up of dynamic layers. The aim is to identify the technological, functional, qualitative and performance parameters that guide the decisions of the actors in the innovation process. It explores the factors that drive them to develop solutions and proposals designed to transform the envelope of the building from a static to a dynamic element, featuring interoperable components that can interact with the input from the outdoor and indoor environments, in relation to which the smart skin acts as a system of boundary and delimitation. The proposed research programme explores in particular the sector of Smart Envelopes, setting as its priority objective the identification and definition of the energy performance, both through analysis of the state of the art and through the development of a facade component that is dynamic in terms of the adaptive variability of its performance.

scholarly journals Specializing Word Embeddings (for Parsing) by Information Bottleneck (Extended Abstract)

2020 ◽  
Author(s):  
Xiang Lisa Li ◽  
Jason Eisner
Keyword(s):  
Dimensionality Reduction ◽  
Semantic Information ◽  
State Of The Art ◽  
Word Embedding ◽  
Discrete Version ◽  
Word Embeddings ◽  
Continuous Version ◽  
Continuous Vector ◽  
Information Bottleneck ◽  
Art Performance

Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser. We compress each word embedding to either a discrete tag or a continuous vector. In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity. In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.

scholarly journals Definition Extraction from Generic and Mathematical Domains with Deep Ensemble Learning

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2502
Author(s):  
Natalia Vanetik ◽  
Marina Litvak
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Binary Classification ◽  
Ensemble Model ◽  
Classification Models ◽  
New Materials ◽  
Educational Materials ◽  
Efficient Learning ◽  
Mathematical Definitions

Definitions are extremely important for efficient learning of new materials. In particular, mathematical definitions are necessary for understanding mathematics-related areas. Automated extraction of definitions could be very useful for automated indexing educational materials, building taxonomies of relevant concepts, and more. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. In this paper, we focus on automatic detection of one-sentence definitions in mathematical and general texts. We experiment with different classification models arranged in an ensemble and applied to a sentence representation containing syntactic and semantic information, to classify sentences. Our ensemble model is applied to the data adjusted with oversampling. Our experiments demonstrate the superiority of our approach over state-of-the-art methods in both general and mathematical domains.

scholarly journals AutoExtend: Combining Word Embeddings with Semantic Resources

2017 ◽  
Vol 43 (3) ◽  
pp. 593-617 ◽  
Author(s):  
Sascha Rothe ◽  
Hinrich Schütze
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
Input Word ◽  
Word Sense ◽  
Word Embeddings ◽  
Training Corpus ◽  
Context Similarity ◽  
Sense Disambiguation ◽  
Semantic Resources

We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks.

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