WiFi SLAM algorithms: an experimental comparison

Robotica ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 837-858 ◽  
Author(s):  
F. Herranz ◽  
A. Llamazares ◽  
E. Molinos ◽  
M. Ocaña ◽  
M. A. Sotelo
Keyword(s):  
Indoor Environment ◽  
State Of The Art ◽  
Experimental Comparison ◽  
Smoothing Algorithm ◽  
Indoor Environments ◽  
Robotic Platform ◽  
Current State ◽  
Localization And Mapping ◽  
Mapping Techniques ◽  
Filtering Approach

SUMMARYLocalization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

scholarly journals A visual SLAM method based on point-line fusion in weak-matching scene

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090419 ◽  
Author(s):  
Baofu Fang ◽  
Zhiqiang Zhan
Keyword(s):  
State Of The Art ◽  
Localization Accuracy ◽  
Line Segments ◽  
Research Areas ◽  
Current State ◽  
Localization And Mapping ◽  
Reprojection Error ◽  
Feature Based ◽  
Point Line ◽  
Point Feature

Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.

scholarly journals 3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chongben Tao ◽  
Yufeng Jin ◽  
Feng Cao ◽  
Zufeng Zhang ◽  
Chunguang Li ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Semantic Information ◽  
Moving Objects ◽  
Experimental Comparison ◽  
Indoor Environments ◽  
Feature Points ◽  
Current Frame ◽  
Semantic Map ◽  
Key Frame ◽  
The Impact

In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes. In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed. Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping. Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained.

scholarly journals Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN

2019 ◽  
Vol 9 (21) ◽  
pp. 4656 ◽  
Author(s):  
Haikel Alhichri ◽  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Bilel Bin Jdira
Keyword(s):  
Deep Learning ◽  
Visually Impaired ◽  
Indoor Environment ◽  
State Of The Art ◽  
Activation Function ◽  
Linear Functions ◽  
Indoor Environments ◽  
Scene Description ◽  
Deep Cnn ◽  
Better Than

This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects, regardless of their position in the scene. This problem is also known as image multi-labeling. (2) Methods: Our proposed deep learning solution is based on a light-weight pre-trained CNN called SqueezeNet. We improved the SqueezeNet architecture by resetting the last convolutional layer to free weights, replacing its activation function from a rectified linear unit (ReLU) to a LeakyReLU, and adding a BatchNormalization layer thereafter. We also replaced the activation functions at the output layer from softmax to linear functions. These adjustments make up the main contributions in this work. (3) Results: The proposed solution is tested on four image multi-labeling datasets representing different indoor environments. It has achieved results better than state-of-the-art solutions both in terms of accuracy and processing time. (4) Conclusions: The proposed deep CNN is an effective solution for predicting the presence of objects in a scene and can be successfully used as a module within BlindSys.

scholarly journals An Adaptive Augmented Vision-Based Ellipsoidal SLAM for Indoor Environments

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2795
Author(s):  
Lahemer ◽  
Rad
Keyword(s):  
Real Time ◽  
Indoor Environment ◽  
Vision System ◽  
Computational Effort ◽  
Monocular Vision ◽  
Indoor Environments ◽  
Simulation Studies ◽  
Localization And Mapping ◽  
Landmark Identification ◽  
Slam Algorithm

In this paper, the problem of Simultaneous Localization And Mapping (SLAM) is addressed via a novel augmented landmark vision-based ellipsoidal SLAM. The algorithm is implemented on a NAO humanoid robot and is tested in an indoor environment. The main feature of the system is the implementation of SLAM with a monocular vision system. Distinguished landmarks referred to as NAOmarks are employed to localize the robot via its monocular vision system. We henceforth introduce the notion of robotic augmented reality (RAR) and present a monocular Extended Kalman Filter (EKF)/ellipsoidal SLAM in order to improve the performance and alleviate the computational effort, to provide landmark identification, and to simplify the data association problem. The proposed SLAM algorithm is implemented in real-time to further calibrate the ellipsoidal SLAM parameters, noise bounding, and to improve its overall accuracy. The augmented EKF/ellipsoidal SLAM algorithms are compared with the regular EKF/ellipsoidal SLAM methods and the merits of each algorithm is also discussed in the paper. The real-time experimental and simulation studies suggest that the adaptive augmented ellipsoidal SLAM is more accurate than the conventional EKF/ellipsoidal SLAMs.

scholarly journals An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4254 ◽  
Author(s):  
Le Jiang ◽  
Pengcheng Zhao ◽  
Wei Dong ◽  
Jiayuan Li ◽  
Mingyao Ai ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Large Scale ◽  
Field Experiments ◽  
Simultaneous Localization And Mapping ◽  
Detection Algorithm ◽  
Indoor Environments ◽  
Practical Applications ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Scanning Detection

Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

scholarly journals RESEARCH ON SEMANTIC-ASSISTED SLAM IN COMPLEX DYNAMIC INDOOR ENVIRONMENT

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 353-359
Author(s):  
C. Li ◽  
Z. Kang ◽  
J. Yang ◽  
F. Li ◽  
Y. Wang
Keyword(s):  
Indoor Environment ◽  
Moving Objects ◽  
Solution Process ◽  
Dynamic Environments ◽  
Satellite System ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Feature Points ◽  
Localization And Mapping ◽  
Object Area

Abstract. Visual Simultaneous Localization and Mapping (SLAM) systems have been widely investigated in response to requirements, since the traditional positioning technology, such as Global Navigation Satellite System (GNSS), cannot accomplish tasks in restricted environments. However, traditional SLAM methods which are mostly based on point feature tracking, usually fail in harsh environments. Previous works have proven that insufficient feature points caused by missing textures, feature mismatches caused by too fast camera movements, and abrupt illumination changes will eventually cause state estimation to fail. And meanwhile, pedestrians are unavoidable, which introduces fake feature associations, thus violating the strict assumption that the unknown environment is static in SLAM. In order to ensure how our system copes with the huge challenges brought by these factors in a complex indoor environment, this paper proposes a semantic-assisted Visual Inertial Odometer (VIO) system towards low-textured scenes and highly dynamic environments. The trained U-net will be used to detect moving objects. Then all feature points in the dynamic object area need to be eliminated, so as to avoid moving objects to participate in the pose solution process and improve robustness in dynamic environments. Finally, the constraints of inertial measurement unit (IMU) are added for low-textured environments. To evaluate the performance of the proposed method, experiments were conducted on the EuRoC and TUM public dataset, and the results demonstrate that the performance of our approach is robust in complex indoor environments.

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 TIMA SLAM: Tracking Independently and Mapping Altogether for an Uncalibrated Multi-Camera System

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 409
Author(s):  
Omer Faruk Ince ◽  
Jun-Sik Kim
Keyword(s):  
Indoor Environment ◽  
State Of The Art ◽  
Experimental Results ◽  
Multiple Cameras ◽  
Camera System ◽  
Localization And Mapping ◽  
Extrinsic Parameters ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

We present a novel simultaneous localization and mapping (SLAM) system that extends the state-of-the-art ORB-SLAM2 for multi-camera usage without precalibration. In this system, each camera is tracked independently on a shared map, and the extrinsic parameters of each camera in the fixed multi-camera system are estimated online up to a scalar ambiguity (for RGB cameras). Thus, the laborious precalibration of extrinsic parameters between cameras becomes needless. By optimizing the map, the keyframe poses, and the relative poses of the multi-camera system simultaneously, observations from the multiple cameras are utilized robustly, and the accuracy of the shared map is improved. The system is not only compatible with RGB sensors but also works on RGB-D cameras. For RGB cameras, the performance of the system tested on the well-known EuRoC/ASL and KITTI datasets that are in the stereo configuration for indoor and outdoor environments, respectively, as well as our dataset that consists of three cameras with small overlapping regions. For the RGB-D tests, we created a dataset that consists of two cameras for an indoor environment. The experimental results showed that the proposed method successfully provides an accurate multi-camera SLAM system without precalibration of the multi-cameras.

scholarly journals Analysing stimuli and barriers, failure and resilience in companies’ internationalization: a systematic and bibliometric review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rui Martins ◽  
Luis Farinha ◽  
Joao J. Ferreira
Keyword(s):  
Design Methodology ◽  
Web Of Science ◽  
State Of The Art ◽  
Scientific Production ◽  
Content Type ◽  
Future Studies ◽  
Main Research ◽  
Current State ◽  
Bibliometric Review ◽  
Mapping Techniques

Purpose This study aims to systematize and analyze the internationalization of companies in an attempt to identify the main themes explored in the literature – What does the current state of the art tell us about the stimuli and barriers, failures and resilience in the internationalization of companies is what the authors set out to answer with this research. Design/methodology/approach This study offers a systematic review based on the bibliometric mapping techniques of 218 articles collected from the Web of Science database between 1996 and 2021. Findings Furthermore, contributing to literature by the logic of stimuli, barriers failure in internationalization companies. The main conclusion is that, from the analysis, the authors can see that the scientific production in this field of knowledge has intensified in recent years, but the centrality and the search density undergo major changes. Originality/value The main research contributions enable a better understanding of the involvement of failure and resilience in companies re-internationalization and suggestions for future studies in this field.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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