A novel data fusion algorithm for low-cost localisation and navigation of autonomous vineyard sprayer robots

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

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Sensor Data Fusion Algorithm for Indoor Environment Mapping Using Low-Cost Sensors

Journal of Control Automation and Electrical Systems ◽

10.1007/s40313-013-0023-4 ◽

2013 ◽

Vol 24 (3) ◽

pp. 199-211 ◽

Cited By ~ 4

Author(s):

Luciano Buonocore ◽

Cairo Lúcio Nascimento Júnior ◽

Areolino de Almeida Neto

Keyword(s):

Data Fusion ◽

Indoor Environment ◽

Low Cost ◽

Sensor Data ◽

Fusion Algorithm ◽

Sensor Data Fusion

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A low-cost IMU/GPS position accuracy experimental study using extended kalman filter data fusion in real environments

E3S Web of Conferences ◽

10.1051/e3sconf/202129701040 ◽

2021 ◽

Vol 297 ◽

pp. 01040

Author(s):

Aziz El Fatimi ◽

Adnane Addaim ◽

Zouhair Guennoun

Keyword(s):

Experimental Study ◽

Data Fusion ◽

Low Cost ◽

Three Dimensional ◽

Measurement Unit ◽

Circular Path ◽

Positioning System ◽

Fusion Algorithm ◽

Position Accuracy ◽

The Stability

In a three-dimensional environment, the navigation of a vehicle in airspace, terrestrial space, or maritime space presents complex aspects concerning the determination of its position, its orientation, and the stability of the processing of the asynchronous data coming from the various sensors during navigation. In this context, this paper presents an experimental analysis of the position accuracy estimated by a low-cost inertial measurement unit coupled, by the extended Kalman data fusion algorithm, with a system of absolute measurements of a positioning system received from a GPS which designates the global positioning system. The different scenarios of the experimental study carried out during this work concerned three tests in a real environment, such as the navigation in a course inside the city of Rabat/Morocco with a moderate speed, a section on the highway at a speed of 120 Km/h and a circular path around a roundabout. The experimental results proved that the low-cost sensors studied are a good candidate for civil navigation applications.

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Navigation Sensor Data Fusion for the AUV Remus

Marine Technology and SNAME News ◽

10.5957/mt1.2001.38.1.65 ◽

2001 ◽

Vol 38 (01) ◽

pp. 65-69

Author(s):

Thomas F. Fulton ◽

Christopher J. Cassidy

Keyword(s):

Data Fusion ◽

Autonomous Underwater Vehicle ◽

Low Cost ◽

Field Trials ◽

Sensor Data ◽

Doppler Velocity ◽

Fusion Algorithm ◽

Sensor Data Fusion ◽

Acoustic Navigation ◽

Using Data

The development of a navigation sensor data fusion algorithm for the autonomous underwater vehicle (AUV) Remus is described. Remus is a small, low-cost AUV designed and built at the Ocean Systems Laboratory of the Woods Hole Oceanographic Institute. The navigation sensors for Remus include an acoustic navigation system, a Doppler velocity sonar, and a compass. The data from these sensors are integrated in an extended Kalman filter, with the objective of producing a more accurate vehicle track. Postprocessing results using data from two recent field trials are presented.

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A SENSOR DATA FUSION ALGORITHM FOR INDOOR SLAM USING A LOW-COST MOBILE ROBOT

Adaptive Mobile Robotics ◽

10.1142/9789814415958_0097 ◽

2012 ◽

pp. 762-769

Author(s):

LUCIANO BUONOCORE ◽

AEROLINO DE ALMEIDA NETO ◽

CAIRO LÚCIO NASCIMENTO JÚNIOR

Keyword(s):

Data Fusion ◽

Mobile Robot ◽

Low Cost ◽

Sensor Data ◽

Fusion Algorithm ◽

Sensor Data Fusion

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Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation

Sensors ◽

10.3390/s20216292 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6292

Author(s):

Vincenzo Di Pietra ◽

Paolo Dabove ◽

Marco Piras

Keyword(s):

Data Fusion ◽

Low Cost ◽

Ground Truth ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Ultra Wideband ◽

Portable Devices ◽

Positioning System ◽

Fusion Algorithm ◽

Satellite Systems

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.

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Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

Sensors ◽

10.3390/s22010391 ◽

2022 ◽

Vol 22 (1) ◽

pp. 391

Author(s):

Zhonghan Li ◽

Yongbo Zhang

Keyword(s):

Data Fusion ◽

Autonomous Navigation ◽

Low Cost ◽

Optimization Method ◽

Satellite System ◽

Fusion Algorithm ◽

K Nearest Neighbors ◽

Positioning Accuracy ◽

Spatial Features ◽

Global Navigation Satellite

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

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