scholarly journals An Indoor Navigation System Based on Stereo Camera and Inertial Sensors with Points and Lines

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Yang ◽  
Xiaosu Xu ◽  
Tao Zhang ◽  
Yao Li ◽  
Jinwu Tong
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Indoor Navigation ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Complex Environments ◽  
Stereo Camera ◽  
Tightly Coupled ◽  
Indoor Navigation System ◽  
Better Than

An indoor navigation system based on stereo camera and inertial sensors with points and lines is proposed to further improve the accuracy and robustness of the navigation system in complex indoor environments. The point and line features, which are fast extracted by ORB method and line segment detector (LSD) method, are both employed in this system to improve its ability to adapt to complex environments. In addition, two different representations of lines are adopted to improve the efficiency of the system. Besides stereo camera, an inertial measurement unit (IMU) is also used in the system to further improve its accuracy and robustness. An estimator is designed to integrate the camera and IMU measurements in a tightly coupled approach. The experimental results show that the performance of the proposed navigation system is better than the point-only VINS and the vision-only navigation system with points and lines.

scholarly journals Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6238
Author(s):  
Payal Mahida ◽  
Seyed Shahrestani ◽  
Hon Cheung
Keyword(s):  
Navigation System ◽  
Visually Impaired ◽  
Indoor Environment ◽  
Inertial Sensor ◽  
User Interaction ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Indoor Environments ◽  
Visually Impaired People ◽  
Indoor Navigation System

Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.

scholarly journals A LiDAR-Aided Indoor Navigation System for UGVs

2014 ◽  
Vol 68 (2) ◽  
pp. 253-273 ◽  
Author(s):  
Shifei Liu ◽  
Mohamed Maher Atia ◽  
Tashfeen B. Karamat ◽  
Aboelmagd Noureldin
Keyword(s):  
Navigation System ◽  
Error Model ◽  
Indoor Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Model Errors ◽  
Integrated Navigation System ◽  
Tightly Coupled ◽  
Indoor Navigation System ◽  
Sensor Errors

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.

scholarly journals DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

2015 ◽  
Vol XL-4/W5 ◽  
pp. 81-86 ◽  
Author(s):  
Y. C. Lai ◽  
C. C. Chang ◽  
C. M. Tsai ◽  
S. Y. Lin ◽  
S. C. Huang
Keyword(s):  
Fuzzy Logic ◽  
Sensor Fusion ◽  
Navigation System ◽  
Indoor Environment ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Navigation ◽  
Estimation Algorithms ◽  
Indoor Navigation System

This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its usability. The experiment results of the proposed navigation system demonstrate good navigation performance in indoor environment with the accurate initial location and direction.

Indoor Navigation System of Omni-Directional Mobile Robot Based on Static Obstacles Avoidance

2020 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Shaaban Ali Salman ◽  
Qais A. Khasawneh ◽  
Mohammad A. Jaradat ◽  
Mansour Y. Alramlawi
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Indoor Navigation ◽  
Indoor Navigation System

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

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