scholarly journals An Unconventional Multiple Low-Cost IMU and GPS-Integrated Kinematic Positioning and Navigation Method Based on Singer Model

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4274
Author(s):  
Zhu ◽  
Yu ◽  
Xiao
Keyword(s):  
Low Cost ◽  
A Priori ◽  
Strong Dependence ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Trajectory Model ◽  
The Road ◽  
Integration Strategy ◽  
Smooth Transitions

To release the strong dependence of the conventional inertial navigation mechanization on the a priori low-cost inertial measurement unit (IMU) error model, this research applies an unconventional multi-sensor integration strategy to integrate multiple low-cost IMUs and a global positioning system (GPS) for mass-market automotive applications. The unconventional integration strategy utilizes a basic three-dimensional (3D) kinematic trajectory model as the system model to directly estimate navigational parameters, and it allows the measurements from all of the sensors independently participating in measurement updates. However, the less complex kinematic model cannot realize smooth transitions between different motion statuses for the road vehicle with acceleration maneuvers. In this manuscript, we establish a more practical 3D kinematic trajectory model based on a “current” statistical Singer acceleration model to realize smooth transitions for the maneuvering vehicle. In addition, taking advantage of the unconventional strategy, we individually model the systematic errors of each IMU and the measurements of all sensors, in contrast to most existing approaches that adopt the common-mode errors for different sensors of the same design. A real dataset involving a GPS and multiple IMUs is processed to validate the success of the proposed algorithm model under the unconventional integration strategy.

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

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

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.

scholarly journals Road Bank Angle Estimation for Three Wheel Tilting Vehicle Using Multi Model Estimator

2018 ◽  
Vol 166 ◽  
pp. 02008
Author(s):  
Kicheol Jeong ◽  
Seibum Choi
Keyword(s):  
Dynamic Model ◽  
Tilt Angle ◽  
Traffic Congestion ◽  
Kinematic Model ◽  
Measurement Unit ◽  
Bank Angle ◽  
Lateral Velocity ◽  
Data Set ◽  
The Road ◽  
Vehicle Acceleration

In recent years, the need for micro-mobility, especially three-wheel vehicles, is increasing to address pollution and traffic congestion problems. With regard to the development of tilting vehicles, the precise tilt angle is important information in the tilting mechanism. Since the road environment affects the vehicle tilt angle, the road bank and hill angle have to be estimated to optimize the tilt control system. Furthermore, a new tilt mechanism is required due to the energy consumption of the previous active tilting mechanism. This paper introduces the road state observer. In this paper, the observer that combines a kinematic model with a dynamic model of a three wheel vehicle is proposed. The dynamic model has four states, including lateral velocity, yaw rate, tilt angle, and tilt angle ratio. Similarly, kinematic model has two states, including roll and pitch angles. It is assumed that the data set received from the six-dimensional inertial measurement unit including the vehicle acceleration and angular velocity of all axes is available. To verify the proposed algorithm, simulation verification using Carsim ADAMS and Matlab&Simulink is performed and a discussion of the result is provided. In addition, this paper proposes a semi-active tilt system.

scholarly journals Low-Cost Monitoring System of Sensors for Evaluating Dynamic Solicitations of Semitrailer Structure

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Pablo Luque ◽  
Daniel A. Mántaras ◽  
Aida Rodríguez ◽  
Hugo Malón ◽  
Luis Castejón ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Monitoring System ◽  
Extended Kalman Filter ◽  
Numerical Models ◽  
Low Cost ◽  
Three Dimensional ◽  
Cost Benefit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Definition Of

Analysis of the fatigue life of a semitrailer structure necessitates identification of the loads and dynamic solicitations in the structure. These forces can be introduced in computer simulation software (multibody + finite element) for analysing the response of different design solutions to them. These numerical models must be validated and some parameters need to be measured directly in a field test with real vehicles under various driving conditions. In this study, a low-cost monitoring system is developed for application to a real fleet of semitrailers. According to the definition of the numerical model, the guidance of a virtual vehicle is defined by the three-dimensional kinematics of the kingpin. For characterisation of these movements, a monitoring system having a low-cost inertial measurement unit (IMU) and global positioning system (GPS) antennas is developed with different configurations to enable analysis of the best cost-benefit (result accuracy) solution, and an extended Kalman filter (EKF) that characterises the kinematic guidance of the kingpin is proposed. A semitrailer was equipped with the experimental low-cost monitoring system and high-precision sensors (IMU, GPS) in order to validate the results obtained by the experimental low-cost monitoring system and the inertial-extended Kalman filter developed. The validated system has applicability in the low-cost monitoring of a fleet of real vehicles.

Novel Integration Strategy for GNSS-Aided Inertial Integrated Navigation

GEOMATICA ◽  
2015 ◽  
Vol 69 (2) ◽  
pp. 217-230 ◽  
Author(s):  
Kun Qian ◽  
Jian-Guo Wang ◽  
Baoxin Hu
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Strong Dependence ◽  
Inertial Navigation ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integration Strategy ◽  
Integration Mechanism

The conventional integration mechanism in GNSS (Global Navigation Satellite Systems) aided inertial integrated positioning and navigation system is mainly based on the continuous outputs of the navigation mechanization, the associated error models for navigation parameters, the biases of the inertial measurement units (IMU), and the error measurements. Its strong dependence on the a priori error characteristics of inertial sensors may suffer with the low-cost IMUs, e.g. the MEMS IMUs due to their low and unstable performance. This paper strives for a significant breakthrough in a compact and general integration strategy which restructures the Kalman filter by deploying a system model on the basis of 3D kinematics of a rigid body and performing measurement update via all sensor data inclusive of the IMU measurements. This novel IMU/GNSS Kalman filter directly estimates navigational parameters instead of the error states. It enables the direct use of the IMU's raw outputs as measurements in measurement updates of Kalman filter instead of involving the free inertial navigation calculation through the conventional integration mechanism. This realization makes all of the sensors in a system no longer to be differentiated between core and aiding sensors. The proposed integration strategy can greatly enhance the sustainability of low-cost navigation systems in poor GNSS and/or GNSS denied environment compared to the conventional aided error-state-based inertial navigation integration mechanism. The post-processed solutions are presented to show the success of the proposed multisensor integrated navigation strategy.

scholarly journals Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

scholarly journals Improving Low-Cost Solutions for Path Mapping in Autonomous Vehicle

2018 ◽  
Vol 7 (4.11) ◽  
pp. 206
Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman ◽  
Juliana Johari ◽  
Syahrul Afzal Che Abdullah ◽  
Nooritawati Md Tahir
Keyword(s):  
Low Cost ◽  
Autonomous Vehicle ◽  
Linear Interpolation ◽  
Measurement Unit ◽  
The Road ◽  
Bias Drift ◽  
Main Challenge ◽  
Localization And Mapping ◽  
Accuracy And Precision ◽  
Mapping Process

One of the main challenge in designing autonomous vehicle is developing the algorithms necessary for simultaneous localization and mapping (SLAM). While this process heavily depends on an expensive hardware called Light Detection and Ranging (LiDAR), there are cheaper alternatives that can be implemented in earlier stage of autonomous vehicle development. Inertial Measurement Unit (IMU), vehicle odometry, and Global Positioning System (GPS) can be used as a solution. The main problem is these cheaper alternatives relies heavily on the precision of the hardware used. In this project, we outline the mapping process using these cheaper solutions which can also be used to complement LiDAR-based SLAM. We show that using simple approaches such as static bias drift removal, high-pass filtering, signal downsampling and linear interpolation, we can increase the robustness and improve the accuracy and precision of the IMU and GPS used respectively. We manage to increase the precision of the GPS readings and reduce the drift of IMU on average from -17.105 deg/min to -0.1177 deg/min. We show the improvement achieved by our proposed method by mapping the road around Engine Square, Jalan Ilmu 1/1, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.  

scholarly journals Validation of Low-Cost Pavement Monitoring Inertial Sensor for Urban Road Network

2021 ◽  
Author(s):  
Giuseppe Loprencipe ◽  
Flavio Guilherme Vaz de Almeida Filho ◽  
Rafael Henrique de Oliveira ◽  
Salvatore Bruno
Keyword(s):  
Ride Comfort ◽  
Inertial Sensor ◽  
Low Cost ◽  
Road Networks ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
Micro Electro Mechanical Systems ◽  
The Road ◽  
Comfort Index ◽  
Rolling Noise

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, an Inertial Measurement Unit is proposed by using a low-cost three-axis Micro-Electro-Mechanical Systems accelerometer and a GPS instrument, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to monitor indirectly the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was considered. Considering 21 km of roads, with different levels of pavement decay, validation measures made using the proposed IMU, another pre-assembled IMU, and a Road Surface Profiler were performed. Therefore, comparisons between awz determined with accelerations measured on the two different IMU are made; in addition, also correlations between awz, International Roughness Index (IRI), and Ride Number (RN) were performed. The results were shown very good correlations between the awz calculated with the proposed IMU and ones in the other IMU. In addition, the correlations between awz and IRI and RN were showed promising results, considering the use and the costs of the proposed IMU as a reliable method to assess the pavements decay in road networks where the use of traditional systems is difficult and/or not cheap.

scholarly journals Optimizing Irradiation Geometry in LED-Based Photoacoustic Imaging with 3D Printed Flexible and Modular Light Delivery System

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3789 ◽  
Author(s):  
Maju Kuriakose ◽  
Christopher D. Nguyen ◽  
Mithun Kuniyil Ajith Singh ◽  
Srivalleesha Mallidi
Keyword(s):  
Angular Dependence ◽  
Photoacoustic Imaging ◽  
Low Cost ◽  
A Priori ◽  
Strong Dependence ◽  
Point Sources ◽  
Tissue Imaging ◽  
Scattering Media ◽  
Laser Systems ◽  
Imaging Plane

Photoacoustic (PA) imaging–a technique combining the ability of optical imaging to probe functional properties of the tissue and deep structural imaging ability of ultrasound–has gained significant popularity in the past two decades for its utility in several biomedical applications. More recently, light-emitting diodes (LED) are being explored as an alternative to bulky and expensive laser systems used in PA imaging for their portability and low-cost. Due to the large beam divergence of LEDs compared to traditional laser beams, it is imperative to quantify the angular dependence of LED-based illumination and optimize its performance for imaging superficial or deep-seated lesions. A custom-built modular 3-D printed hinge system and tissue-mimicking phantoms with various absorption and scattering properties were used in this study to quantify the angular dependence of LED-based illumination. We also experimentally calculated the source divergence of the pulsed-LED arrays to be 58° ± 8°. Our results from point sources (pencil lead phantom) in non-scattering medium obey the cotangential relationship between the angle of irradiation and maximum PA intensity obtained at various imaging depths, as expected. Strong dependence on the angle of illumination at superficial depths (−5°/mm at 10 mm) was observed that becomes weaker at intermediate depths (−2.5°/mm at 20 mm) and negligible at deeper locations (−1.1°/mm at 30 mm). The results from the tissue-mimicking phantom in scattering media indicate that angles between 30–75° could be used for imaging lesions at various depths (12 mm–28 mm) where lower LED illumination angles (closer to being parallel to the imaging plane) are preferable for deep tissue imaging and superficial lesion imaging is possible with higher LED illumination angles (closer to being perpendicular to the imaging plane). Our results can serve as a priori knowledge for the future LED-based PA system designs employed for both preclinical and clinical applications.

Nonholonomic virtual constraints and gait optimization for robust walking control

2017 ◽  
Vol 36 (8) ◽  
pp. 895-922 ◽  
Author(s):  
Brent Griffin ◽  
Jessy Grizzle
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Optimization Method ◽  
Measurement Unit ◽  
Feedback Controllers ◽  
Environment Model ◽  
Simulation And Experiment ◽  
Walking Speeds ◽  
Posture Regulation ◽  
Gait Optimization

A key challenge in robotic bipedal locomotion is the design of feedback controllers that function well in the presence of uncertainty, in both the robot and its environment. This paper addresses the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without over-reliance on perception and a priori knowledge of the environment. Model-based design methods are introduced and subsequently validated in simulation and experiment on MARLO, an underactuated three-dimensional bipedal robot that is of roughly human size and is equipped with an inertial measurement unit and joint encoders. Innovations include an optimization method that accounts for multiple types of disturbances and a feedback control design that enables continuous velocity-based posture regulation via nonholonomic virtual constraints. Using a single continuously defined controller taken directly from optimization, MARLO traverses sloped sidewalks and parking lots, terrain covered with randomly thrown boards, and grass fields, all while maintaining average walking speeds between 0.9 and 0.98 m/s and setting a new precedent for walking efficiency in realistic environments.

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