scholarly journals Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1770 ◽  
Author(s):  
Lingyu Yang ◽  
Xiaoke Feng ◽  
Jing Zhang ◽  
Xiangqian Shu
Keyword(s):  
Indoor Localization ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Ultrasonic Sensors ◽  
Sensor Model ◽  
Air Vehicle ◽  
Measurement Function ◽  
Simulation And Experiment ◽  
Localization Performance ◽  
Localization Approach

Due to its payload, size and computational limits, localizing a micro air vehicle (MAV) using only its onboard sensors in an indoor environment is a challenging problem in practice. This paper introduces an indoor localization approach that relies on only the inertial measurement unit (IMU) and four ultrasonic sensors. Specifically, a novel multi-ray ultrasonic sensor model is proposed to provide a rapid and accurate approximation of the complex beam pattern of the ultrasonic sensors. A fast algorithm for calculating the Jacobian matrix of the measurement function is presented, and then an extended Kalman filter (EKF) is used to fuse the information from the ultrasonic sensors and the IMU. A test based on a MaxSonar MB1222 sensor demonstrates the accuracy of the model, and a simulation and experiment based on the T h a l e s I I MAV platform are conducted. The results indicate good localization performance and robustness against measurement noises.

scholarly journals Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4128 ◽  
Author(s):  
Irem Uygur ◽  
Renato Miyagusuku ◽  
Sarthak Pathak ◽  
Alessandro Moro ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Semantic Information ◽  
Indoor Localization ◽  
Low Cost ◽  
Large Data ◽  
Point Clouds ◽  
Measurement Unit ◽  
Sensor Model ◽  
Localization Approach ◽  
Spherical Camera

Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

An EKF-based multiple data fusion for mobile robot indoor localization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guangbing Zhou ◽  
Jing Luo ◽  
Shugong Xu ◽  
Shunqing Zhang ◽  
Shige Meng ◽  
...  
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Indoor Localization ◽  
Robot Navigation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Content Type ◽  
Multiple Data

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

scholarly journals A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 193 ◽  
Author(s):  
Haibin Tong ◽  
Ning Xin ◽  
Xianli Su ◽  
Tengfeng Chen ◽  
Jingjing Wu
Keyword(s):  
High Precision ◽  
Indoor Localization ◽  
Poor Performance ◽  
Harsh Environments ◽  
Line Of Sight ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Advantages And Disadvantages ◽  
Hybrid Localization ◽  
Localization Approach

Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.

Localization of a Miniature Spherical Rolling Robot Using IMU, Odometry and UWB

2018 ◽  
Author(s):  
Van Duong Nguyen ◽  
Gim Song Soh ◽  
Shaohui Foong ◽  
Kristin Wood
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Spherical Body ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Spherical Robot ◽  
Intelligent Surveillance ◽  
Localization Performance ◽  
High Degree ◽  
Rolling Robot

Robots that rolls with a spherical body or spherical robots, exhibits a high degree of mobility and amazing recovery capability from collisions while traversing in the environment. However, the localization of spherical robots in a GPS-denied environment for Intelligent Surveillance and Reconnaissance (ISR) task is a challenging problem due to the complexity of its system dynamics and the limited available sensors technology to sense out of the spherical shell. In our prior work, a kinematic localization technique based on odometry and inertial measurement unit (IMU) sensing was proposed and implemented onto our miniature spherical robot Virgo, for pose estimation. However, it suffers from errors due to slippages during locomotion or as a result of the collision. In this paper, we present a solution to this problem by the inclusion of an additional ultra-wideband (UWB) sensor and fuse it with our kinematic pose estimator using Extended Kalman Filter for indoor localization. Experiments are conducted on a multi-waypoint trajectory to verify its validity and had shown to improve localization performance.

Beacon selection and calibration for the efficient localization of a mobile robot

Robotica ◽  
2013 ◽  
Vol 32 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Jaehyun Park ◽  
Jangmyung Lee
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Setup Time ◽  
Ultrasonic Waves ◽  
Mobile Robot Navigation ◽  
Indoor Environments ◽  
Ultrasonic Sensors ◽  
Indoor Space ◽  
Localization Scheme ◽  
Set Up

SUMMARYThis paper proposes a localization scheme using ultrasonic beacons in an unstructured multi-block workspace. Indoor localization schemes using ultrasonic sensors have widely been studied due to their low costs and high accuracies. However, ultrasonic sensors are susceptible to environmental noise due to the propagation characteristics of ultrasonic waves. In addition, the decay of ultrasonic signals over long distances implies that ultrasonic sensors are unsuitable for use in large indoor environments. To overcome these shortcomings of ultrasonic sensors, while retaining their advantages, a multi-block approach was devised by dividing an indoor space into several blocks with multiple beacons in each block. However, it is difficult to divide an indoor space into several blocks when beacons cannot be installed in a regular manner or when some new beacons are installed. To resolve this difficulty, a dynamic algorithm is needed to divide an indoor space into multiple blocks and to select suitable beacons. Therefore, this paper proposes a real-time localization scheme to estimate the position of a mobile robot independent of beacon locations and to estimate the position of a new beacon installed at an unknown position. A beacon selection algorithm was developed to select optimal beacons according to robot position and to set up sets of beacons for mobile robot navigation. By using the new beacon searching and calibration algorithm, a mobile robot is able to navigate in an unknown space without requiring the additional setup time needed to install new beacons. The performance of the proposed localization system was verified using real experiments.

scholarly journals NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7886
Author(s):  
Jieum Hyun ◽  
Hyun Myung
Keyword(s):  
Satellite System ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Interacting Multiple Model ◽  
Indoor Environments ◽  
Multiple Model ◽  
Localization Performance ◽  
The One ◽  
Global Navigation Satellite ◽  
Factor Estimation

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

scholarly journals A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3406
Author(s):  
Jie Jiang ◽  
Yin Zou ◽  
Lidong Chen ◽  
Yujie Fang
Keyword(s):  
Image Retrieval ◽  
Indoor Localization ◽  
Data Sets ◽  
Indoor Environments ◽  
Global Features ◽  
Data Set ◽  
Data Annotation ◽  
Wide Range ◽  
Annotation Costs ◽  
Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

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