Sensor fusion of INS, odometer and GPS for robot localization

Yousuf, Sofia; Kadri, Muhammad Bilal

doi:10.1109/spc.2016.7920715

Cited by 26 publications

(10 citation statements)

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“…Furthermore, robots are equipped (or can be extended) with a GPS sensor, which is used to estimate the their absolute position on earth's surface based on received satellite signals [12]. The time context of these measurements is important: when the measurements of the above sensors correspond to the same time window, fusing them leads to an optimal estimate of the exact position of the robot [14].…”

Section: Motivating Examplementioning

confidence: 99%

“…Instead, missing GPS data can be predicted and fused with the readings of the INS and wheel encoders, e.g. using a Kalman Filter [14], [5], to maintain an optimal estimate of a robot's exact position at all times, When sensor data arrives late, it can still be used to obtain a more accurate historic view of a robot's position. In particular, previously computed position estimates that were based on predictions can be recomputed using the late sensor measurements.…”

Section: Motivating Examplementioning

confidence: 99%

See 1 more Smart Citation

Reactive Programming Extensions for Time-Sensitive IoT Applications

Peros

Hughes

2021

2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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Section: Motivating Examplementioning

confidence: 99%

Section: Motivating Examplementioning

confidence: 99%

Reactive Programming Extensions for Time-Sensitive IoT Applications

Peros

Hughes

2021

2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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“…33 The prediction power of this filter has been applied in many assisted and automated navigation and tracking problems. 26,30,[34][35][36][37][38][39][40][41][42][43] Many research studies have reported GPS-INS and GPS-odometer integration schemes utilizing KFs for effective localization and positioning. 14,23,25 It is observed that the KFbased fusion proves effective in reducing the error accumulation in positioning due to INS and odometer at an acceptable level.…”

Section: Multisensor Data Fusionmentioning

confidence: 99%

“…Whereas the process noise covariance matrix Q in above equations is decreased to obtain smoother measurements from KFs. [32][33][34][35][36][37][38][39][40]…”

Section: Process and Measurement Models For The Kfsmentioning

confidence: 99%

Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications

Yousuf¹,

Kadri²

2020

Robotica

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SUMMARY In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

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“…Due to its simplicity and low cost [2], the odometer is still widely used to calculate the velocity of robots. To do so, odometers can be attached to the wheels (or motors) in order to measure their angular velocities.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

et al. 2019

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This paper analyses the impact of parametric uncertainties of a mobile robot kinematic model on velocity and pose estimation, providing models and quantifiable knowledge about them. Most works neglect how the uncertainty regarding the robot's construction aspects (such as wheels radii, the distance between them, and the robot's center of mass) affects both velocities and pose estimation. To help readers understand the influence of such parametric uncertainties, we performed experiments in a simulator and used the collected data on the proposed models. The paper also analyses how the magnitude of velocities considered by the controllers and the followed path can decrease or increase the impact of the parametric uncertainties. The proposed models and presented analysis help understanding the influence (isolated or simultaneous) of different sources of uncertainty in the robot's velocities and pose estimation. This knowledge can be applied to estimate uncertainties for localization methods based on data fusion, complementing or even avoiding the experimental procedures. Also, the development of controllers, robot simulators and new methodologies for parameters' calibration and the design and construction of new robots can also profit from the results presented in this paper.

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Sensor fusion of INS, odometer and GPS for robot localization

Cited by 26 publications

References 1 publication

Reactive Programming Extensions for Time-Sensitive IoT Applications

Reactive Programming Extensions for Time-Sensitive IoT Applications

Information Fusion of GPS, INS and Odometer Sensors for Improving Localization Accuracy of Mobile Robots in Indoor and Outdoor Applications

The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation

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