A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

2011 ◽  
Vol 25 (11) ◽  
pp. 2907-2917 ◽  
Author(s):  
Bong-Su Cho ◽  
Woo-sung Moon ◽  
Woo-Jin Seo ◽  
Kwang-Ryul Baek
Keyword(s):  
Mobile Robots ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Localization System ◽  
Wheel Revolution

A localization system based on buried magnets and dead reckoning for mobile robots

2010 ◽  
Author(s):  
Valery Bourny ◽  
Thierry Capitaine ◽  
Ludovic Barrandon ◽  
Claude Pegard ◽  
Aurelien Lorthois
Keyword(s):  
Mobile Robots ◽  
Dead Reckoning ◽  
Localization System

scholarly journals Proposal for a Localization System for an IoT Ecosystem

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3016
Author(s):  
Juraj Machaj ◽  
Peter Brida ◽  
Slavomir Matuska
Keyword(s):  
Service Providers ◽  
Inertial Sensors ◽  
Multipath Propagation ◽  
Dead Reckoning ◽  
Global Navigation Satellite Systems ◽  
High Signal ◽  
Indoor Environments ◽  
Localization System ◽  
Battery Capacity ◽  
Iot Devices

In the last decade, positioning using wireless signals has gained a lot of attention since it could open new opportunities for service providers. Localization is important, especially in indoor environments, where the widely used global navigation satellite systems (GNSS) signals suffer from high signal attenuation and multipath propagation, resulting in poor accuracy or a loss of positioning service. Moreover, in an Internet of things (IoT) environment, the implementation of GNSS receivers into devices may result in higher demands on battery capacity, as well as increased cost of the hardware itself. Therefore, alternative localization systems that are based on wireless signals for the communication of IoT devices are gaining a lot of attention. In this paper, we provide a design of an IoT localization system, which consists of multiple localization modules that can be utilized for the positioning of IoT devices that are connected thru various wireless technologies. The proposed system can currently perform localization based on received signals from LoRaWAN, ZigBee, Wi-Fi, UWB and cellular technologies. The implemented pedestrian dead reckoning algorithm can process the data measured by a mobile device that is equipped with inertial sensors to construct a radio map and thus help with the deployment of the positioning services based on a fingerprinting approach.

scholarly journals End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi

2021 ◽  
Author(s):  
Mukhamet Nurpeiissov ◽  
Askat Kuzdeuov ◽  
Aslan Assylkhanov, ◽  
Yerbolat Khassanov ◽  
Hüseyin Atakan Varol
Keyword(s):  
Indoor Localization ◽  
Inertial Sensors ◽  
Sampling Rate ◽  
Reference Points ◽  
Measurement Unit ◽  
Localization System ◽  
Practical Applications ◽  
External Data ◽  
End To End ◽  
Commercial Off The Shelf

This paper addresses sequential indoor localization using WiFi and Inertial Measurement Unit (IMU) modules commonly found in commercial off-the-shelf smartphones. Specifically, we developed an end-to-end neural network-based localization system integrating WiFi received signal strength indicator (RSSI) and IMU data without external data fusion models. The developed system leverages the advantages of WiFi and IMU modules to locate finer-level sequential positions of a user at 150 Hz sampling rate. Additionally, to demonstrate the efficacy of the proposed approach, we created the IMUWiFine dataset comprising IMU and WiFi RSSI readings sequentially collected at fine-level reference points. The dataset contains 120 trajectories covering an aggregate distance of over 14 kilometers. We conducted extensive experiments using deep learning models and achieved a mean error distance of 1.1 meters on an unseen evaluation set, which makes our approach suitable for many practical applications requiring meter-level accuracy. To enable experiment and result reproducibility, we made the developed localization system and IMUWiFine dataset publicly available in our GitHub repository.<br>

The Correcting Approach of Gyroscope-Free Inertial Navigation Based on the Applicable Topological Map

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Mehdi Dehghani ◽  
Hamed Kharrati ◽  
Hadi Seyedarabi ◽  
Mahdi Baradarannia
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Inertial Sensors ◽  
Low Cost ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Topological Map ◽  
Stereo Cameras ◽  
Long Time ◽  
Navigation Errors

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.

A Combined Location Method for Mobile Robots Based on Dead Reckoning and WLAN

2014 ◽  
Vol 631-632 ◽  
pp. 649-653 ◽  
Author(s):  
Fang Jia ◽  
Kui Liu ◽  
De Cheng Xu
Keyword(s):  
Mobile Robots ◽  
Information Fusion ◽  
Low Cost ◽  
Dead Reckoning ◽  
Probability Matching ◽  
Location Method ◽  
Indoor Location ◽  
Location Algorithm ◽  
Location Methods ◽  
Received Signal Strength Indication

To minimize the deficiency of the existing indoor location methods for mobile robots, the RSSI (received signal strength indication) model of WLAN is established. Then a combined location method for mobile robots based on DR (dead reckoning) and WLAN is proposed, which employs PMLA (probability matching location algorithm) and KF (Kalman filter) for information fusion. Simulation results reveal that the combined location approach works well in eliminating the cumulative error of DR and reducing the fluctuation of WLAN location. As a result, the proposed method is capable of enhancing the positioning accuracy of mobile robots to a certain extent, promising a low-cost and reliable location scheme for its development.

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