A Comparison between Nonlinear Estimation based Algorithms for Mobile Robot Localizations

Medewar, Prashant G.; Yadav, Manish; Patel, Himanshu

doi:10.1109/icesip46348.2019.8938237

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…This result is important because it indicates that the UT can be used to choose locations to obtain results similar to those obtained using the Monte Carlo method. It is worth noting that the UT has traditionally been used in problems related to parameter estimation, filtering, automation, and control [21][22][23][24][25]. In the last years, it has been used in a variety of new problems, including some in the telecommunications sector as an alternative to Monte Carlo methods [26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Using the unscented transform to reduce the number of measurements in drive tests

2021

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In a drive test, it is common to measure the electric field strength (or other related quantity) at fixed intervals to calibrate propagation models or to optimize wireless network coverage. We propose to select the measurement locations based on the unscented transform. Using just a few points (tens rather than thousands), we show that the statistics of the measurement tends to the correct value, which can save time and reduce the cost of drive tests.

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Section: Discussionmentioning

confidence: 99%

Using the unscented transform to reduce the number of measurements in drive tests

2021

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“…where δp e = [δϕ, δλ, δh] T are the position error states, δv e = [δv N , δv E , δv D ] T are the velocity error states, and δA e = [δp, δr, δy] T are the attitude error states. Furthermore, F is the dynamic coefficient matrix and can described as in Equation (13).…”

Section: State Representationmentioning

confidence: 99%

“…Thus, the UKF provides a better estimation, unlike the EKF which only uses a first order approximation. However, the UKF process time and computational cost are large compared to the EKF [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration

Ibrahim

Abosekeen

Azouz

et al. 2023

Sensors

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High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle’s onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems’ drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from 19.4 m to 3.3 m with 82.98% improvement and the 2D-position max error reduced from 73.9 m to 14.2 m with 80.78% improvement.

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“…Then the finally updated states of the Kalman filter are: (6) This algorithm has poor performance with nonlinear systems that is its main limitation [106].…”

Section: B Localizationmentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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A Comparison between Nonlinear Estimation based Algorithms for Mobile Robot Localizations

Cited by 8 publications

References 22 publications

Using the unscented transform to reduce the number of measurements in drive tests

Using the unscented transform to reduce the number of measurements in drive tests

Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

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