A Practical Low-Cost Navigation System for RGBD Based Mobile Robotics

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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Low-cost and high-performance visual guidance and navigation system for space debris removal

Advanced Robotics ◽

10.1080/01691864.2021.1976671 ◽

2021 ◽

pp. 1-9

Author(s):

Shinichi Kimura ◽

Eijiro Atarashi ◽

Taro Kashiwayanagi ◽

Kohei Fujimoto ◽

Ryan Proffitt

Keyword(s):

Navigation System ◽

High Performance ◽

Space Debris ◽

Low Cost ◽

Visual Guidance ◽

Debris Removal

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VECTOR MAPS IN MOBILE ROBOTICS

Acta Polytechnica CTU Proceedings ◽

10.14311/app.2015.1.0022 ◽

2015 ◽

Vol 2 (2) ◽

pp. 22-28 ◽

Cited By ~ 5

Author(s):

Ales Jelinek

Keyword(s):

Data Acquisition ◽

Navigation System ◽

Mobile Robotics ◽

Map Building ◽

Scan Matching ◽

Line Extraction ◽

Current State ◽

Vector Maps ◽

Localization And Mapping ◽

University Of Technology

The aim of this paper is to provide a brief overview of vector map techniques used in mobile robotics and to present current state of the research in this field at the Brno University of Technology. Vector maps are described as a part of the simultaneous localization and mapping (SLAM) problem in the environment without artificial landmarks or global navigation system. The paper describes algorithms from data acquisition to map building but particular emphasis is put on segmentation, line extraction and scan matching algorithms. All significant algorithms are illustrated with experimental results.

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Seamless outdoor/indoor navigation with WIFI/GPS aided low cost Inertial Navigation System

Physical Communication ◽

10.1016/j.phycom.2013.12.003 ◽

2014 ◽

Vol 13 ◽

pp. 31-43 ◽

Cited By ~ 41

Author(s):

Jiantong Cheng ◽

Ling Yang ◽

Yong Li ◽

Weihua Zhang

Keyword(s):

Navigation System ◽

Inertial Navigation System ◽

Low Cost ◽

Inertial Navigation ◽

Indoor Navigation

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MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2010-0001 ◽

2010 ◽

Vol 34 (1) ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Lucian T. Grigorie ◽

Ruxandra M. Botez

Keyword(s):

Navigation System ◽

Inertial Navigation System ◽

Lower Cost ◽

Inertial Sensors ◽

Low Cost ◽

Inertial Navigation ◽

Noise Power ◽

Integration Algorithm ◽

The Cost ◽

Miniaturized Sensors

In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.

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Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

Sensors ◽

10.3390/s18113673 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3673 ◽

Cited By ~ 2

Author(s):

Zhili Long ◽

Ronghua He ◽

Yuxiang He ◽

Haoyao Chen ◽

Zuohua Li

Keyword(s):

Feature Extraction ◽

Clustering Algorithm ◽

Mobile Robotics ◽

Low Cost ◽

Fuzzy Model ◽

Fuzzy Modeling ◽

Fuzzy Classification ◽

Classification Model ◽

Service Robot ◽

Ultrasonic Array

This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.

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