Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications

Robotica ◽  
2020 ◽  
pp. 1-27
Author(s):  
Sofia Yousuf ◽  
Muhammad Bilal Kadri
Keyword(s):  
Mobile Robot ◽  
Inertial Sensors ◽  
Indoor Environments ◽  
Signal Loss ◽  
Localization Accuracy ◽  
Multiple Sensors ◽  
The Neural Network ◽  
Outdoor Environments ◽  
The Individual ◽  
Proprioceptive Sensors

SUMMARY In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

scholarly journals Neural network assisted Kalman filter for INS/UWB integrated seamless quadrotor localization

2021 ◽  
Vol 7 ◽  
pp. e630
Author(s):  
Shuhui Bi ◽  
Liyao Ma ◽  
Tao Shen ◽  
Yuan Xu ◽  
Fukun Li
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Wide Band ◽  
Indoor Environments ◽  
Position Information ◽  
Localization Accuracy ◽  
The Neural Network ◽  
Measurement Vector ◽  
Localization Information ◽  
Real Test

Due to some harsh indoor environments, the signal of the ultra wide band (UWB) may be lost, which makes the data fusion filter can not work. For overcoming this problem, the neural network (NN) assisted Kalman filter (KF) for fusing the UWB and the inertial navigation system (INS) data seamlessly is present in this work. In this approach, when the UWB data is available, both the UWB and the INS are able to provide the position information of the quadrotor, and thus, the KF is used to provide the localization information by the fusion of position difference between the INS and the UWB, meanwhile, the KF can provide the estimation of the INS position error, which is able to assist the NN to build the mapping between the state vector and the measurement vector off-line. The NN can estimate the KF’s measurement when the UWB data is unavailable. For confirming the effectiveness of the proposed method, one real test has been done. The test’s results demonstrate that the proposed NN assisted KF is effective to the fusion of INS and UWB data seamlessly, which shows obvious improvement of localization accuracy. Compared with the LS-SVM assisted KF, the proposed NN assisted KF is able to reduce the localization error by about 54.34%.

scholarly journals Survey of Millimeter-Wave Propagation Measurements and Models in Indoor Environments

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1653
Author(s):  
Ahmed Al-Saman ◽  
Michael Cheffena ◽  
Olakunle Elijah ◽  
Yousef A. Al-Gumaei ◽  
Sharul Kamal Abdul Rahim ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Measurement Techniques ◽  
Path Loss ◽  
Delay Spread ◽  
Indoor Environments ◽  
Future Trends ◽  
Signal Loss ◽  
Indoor Wireless ◽  
The Future ◽  
Future Demands

The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond.

Feasibility Study of the Form and Structure of a Ball-like Mobile Robot for Indoor Environments

2006 ◽  
Author(s):  
Marek Matusiak ◽  
Pekka Appelqvist ◽  
Janne Paanajarvi ◽  
Tomi Ylikorpi ◽  
Aarne Halme
Keyword(s):  
Mobile Robot ◽  
Feasibility Study ◽  
Indoor Environments

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

2019 ◽  
Vol 13 (1) ◽  
pp. 47-61
Author(s):  
Guenther Retscher ◽  
Jonathan Kleine ◽  
Lisa Whitemore
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Ground Truth ◽  
Mobile User ◽  
Location Based Services ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Point Location ◽  
Point Determination ◽  
The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

scholarly journals Multi-Focal Vision and Gaze Control Improve Navigation Performance

10.5772/50920 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 25 ◽  
Author(s):  
Kolja Kühnlenz ◽  
Martin Buss
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Active Vision ◽  
Path Following ◽  
Mobile Robot Navigation ◽  
Vision Systems ◽  
Localization Accuracy ◽  
Localization And Mapping ◽  
Novel Concept ◽  
The Impact

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

scholarly journals Augmenting ViSP’s 3D Model-Based Tracker with RGB-D SLAM for 3D Pose Estimation in Indoor Environments

2016 ◽  
Vol XLI-B1 ◽  
pp. 925-932
Author(s):  
J. Li-Chee-Ming ◽  
C. Armenakis
Keyword(s):  
Pose Estimation ◽  
Visual Servoing ◽  
3D Model ◽  
Field Of View ◽  
Indoor Environments ◽  
Camera Motion ◽  
3D Pose Estimation ◽  
Building Models ◽  
Outdoor Environments ◽  
Model Features

This paper presents a novel application of the Visual Servoing Platform’s (ViSP) for pose estimation in indoor and GPS-denied outdoor environments. Our proposed solution integrates the trajectory solution from RGBD-SLAM into ViSP’s pose estimation process. Li-Chee-Ming and Armenakis (2015) explored the application of ViSP in mapping large outdoor environments, and tracking larger objects (i.e., building models). Their experiments revealed that tracking was often lost due to a lack of model features in the camera’s field of view, and also because of rapid camera motion. Further, the pose estimate was often biased due to incorrect feature matches. This work proposes a solution to improve ViSP’s pose estimation performance, aiming specifically to reduce the frequency of tracking losses and reduce the biases present in the pose estimate. This paper explores the integration of ViSP with RGB-D SLAM. We discuss the performance of the combined tracker in mapping indoor environments and tracking 3D wireframe indoor building models, and present preliminary results from our experiments.

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