scholarly journals VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4386
Author(s):  
Jingzhe Wang ◽  
Leilei Li ◽  
Huan Yu ◽  
Xunya Gui ◽  
Zucheng Li
Keyword(s):  
Large Scale ◽  
Linear Optimization ◽  
Position Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Non Linear ◽  
Outdoor Environments ◽  
Novel Method ◽  
And Performance ◽  
Inertial Measurements

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system’s viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

COMBINING FLUX AND ENERGY BALANCE ANALYSIS TO MODEL LARGE-SCALE BIOCHEMICAL NETWORKS

2006 ◽  
Vol 04 (06) ◽  
pp. 1227-1243 ◽  
Author(s):  
WILLIAM J. HEUETT ◽  
HONG QIAN
Keyword(s):  
Energy Balance ◽  
Large Scale ◽  
Heat Dissipation ◽  
Linear Optimization ◽  
Biochemical Networks ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Stoichiometric Matrix ◽  
Non Linear ◽  
Detailed Kinetics

Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings.

Riding Motion Capture System Using Inertial Measurement Units with Contact Constraints

2019 ◽  
Vol 13 (4) ◽  
pp. 506-516 ◽  
Author(s):  
Tsubasa Maruyama ◽  
Mitsunori Tada ◽  
Haruki Toda ◽  
◽  
Keyword(s):  
Motion Capture ◽  
Bicycle Ergometer ◽  
Human Motion ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Outdoor Environments ◽  
Optical Motion ◽  
Optical Motion Capture

The measurement of human motion is an important aspect of ergonomic mobility design, in which the mobility product is evaluated based on human factors obtained by digital human (DH) technologies. The optical motion-capture (MoCap) system has been widely used for measuring human motion in laboratories. However, it is generally difficult to measure human motion using mobility products in real-world scenarios, e.g., riding a bicycle on an outdoor slope, owing to unstable lighting conditions and camera arrangements. On the other hand, the inertial-measurement-unit (IMU)-based MoCap system does not require any optical devices, providing the potential for measuring riding motion even in outdoor environments. However, in general, the estimated motion is not necessarily accurate as there are many errors due to the nature of the IMU itself, such as drift and calibration errors. Thus, it is infeasible to apply the IMU-based system to riding motion estimation. In this study, we develop a new riding MoCap system using IMUs. The proposed system estimates product and human riding motions by combining the IMU orientation with contact constraints between the product and DH, e.g., DH hands in contact with handles. The proposed system is demonstrated with a bicycle ergometer, including the handles, seat, backrest, and foot pedals, as in general mobility products. The proposed system is further validated by comparing the estimated joint angles and positions with those of the optical MoCap for three different subjects. The experiment reveals both the effectiveness and limitations of the proposed system. It is confirmed that the proposed system improves the joint position estimation accuracy compared with a system using only IMUs. The angle estimation accuracy is also improved for near joints. However, it is observed that the angle accuracy decreases for a few joints. This is explained by the fact that the proposed system modifies the orientations of all body segments to satisfy the contact constraints, even if the orientations of a few joints are correct. This further confirms that the elapsed time using the proposed system is sufficient for real-time application.

Designing optimal universal pulses using second-order, large-scale, non-linear optimization

2012 ◽  
Vol 219 ◽  
pp. 61-74 ◽  
Author(s):  
Christopher Kumar Anand ◽  
Alex D. Bain ◽  
Andrew Thomas Curtis ◽  
Zhenghua Nie
Keyword(s):  
Large Scale ◽  
Linear Optimization ◽  
Second Order ◽  
Non Linear

scholarly journals NON LINEAR OPTIMIZATION APPLIED TO ANGLE-OF-ARRIVAL SATELLITE BASED GEO-LOCALIZATION FOR BIASED AND TIME-DRIFTING SENSORS

2016 ◽  
Vol XLI-B2 ◽  
pp. 319-325
Author(s):  
Daniel Levy ◽  
Jason Roos ◽  
Jace Robinson ◽  
William Carpenter ◽  
Richard Martin ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Linear Optimization ◽  
Measurement Unit ◽  
Angle Of Arrival ◽  
Multiple Sensors ◽  
Sensing Platform ◽  
Sensor Platform ◽  
Non Linear ◽  
Time Drift ◽  
Infrared Frequencies

Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm.

A method for UAV multi-sensor fusion 3D-localization under degraded or denied GPS situation

2018 ◽  
Vol 6 (3) ◽  
pp. 155-176 ◽  
Author(s):  
Thanabadee Bulunseechart ◽  
Pruittikorn Smithmaitrie
Keyword(s):  
Position Estimation ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Complex Environments ◽  
Indicator Method ◽  
3D Localization ◽  
Gps Measurement ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Optical Flow Sensor

Unmanned aerial vehicles (UAVs) have been developed to be used in complex environments. Continuity of a UAV operation when GPS is degraded or denied is crucial in many applications, such as flying near high buildings and trees, or flying outdoor-to-indoor. In this paper, an algorithm for 3D-localization during transition between indoor and outdoor environments for a UAV is presented. Localization inputs are based on information from GPS, inertial measurement unit, monocular camera, and optical flow sensor. Information is carefully selected using GPS quality indicator method corresponding to the operating environment. After that, a smoothing offset approach is employed to smooth the position estimation. The selected sensors’ data are filtered by indirect extended Kalman filter for localization and extrinsic sensor calibration in real time. Results show a seamless offset convergence of UAV localization for indoor–outdoor transition. Moreover, the proposed method of decision-making to cut off GPS measurement even when it experiences poor signal quality can still outperform conventional GPS-based cutoff method in terms of response time.

scholarly journals NON LINEAR OPTIMIZATION APPLIED TO ANGLE-OF-ARRIVAL SATELLITE BASED GEO-LOCALIZATION FOR BIASED AND TIME-DRIFTING SENSORS

2016 ◽  
Vol XLI-B2 ◽  
pp. 319-325
Author(s):  
Daniel Levy ◽  
Jason Roos ◽  
Jace Robinson ◽  
William Carpenter ◽  
Richard Martin ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Linear Optimization ◽  
Measurement Unit ◽  
Angle Of Arrival ◽  
Multiple Sensors ◽  
Sensing Platform ◽  
Sensor Platform ◽  
Non Linear ◽  
Time Drift ◽  
Infrared Frequencies

Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm.

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