Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction

Cotugno, Giuseppe; D’Alfonso, Luigi; Lúcia, Walter; Muraca, P.; Pugliese, Paolo

doi:10.1109/med.2013.6608693

Cited by 10 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, inappropriate alteration of the noise covariance may result in filter divergence over time, resulting in the complete system becoming unstable. The researchers presented some alternate methods that are moderately straightforward but severe computationally which have the benefit to accommodate the noise model other than the Gaussian such as UKF, FastSLAM, and Monte Carlo localization [24][25][26]. Wireless Communications and Mobile Computing…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

show abstract

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Ullah

Zhang

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…• Problems with the cooperative and collaborative functions like scheduling [12], task allocation [14], path-planning [2] • Problems related to individual robots like localisation [5], dynamic and physical parameters identification and estimating [7], position and orientation estimation [4] and obstacle estimation [8] However, none of these proposed any solution for estimation of cost parameters, proposed here.In this work, unlike the previous work on parameter estimations, we investigate and find an approach to estimate these time-varying cost parameters which are actually derived from the robot level of each AGV as a result of functions done by the actuators and sensors in each AGV. But we are identifying and estimating them at the higher decision making levels to make better cost efficient functions at the robot level, so as to provide the necessary bridge between these two categories of works.…”

Section: Cost Parametersmentioning

confidence: 99%

A study of time-varying cost parameter estimation methods in automated transportation systems based on mobile robots

Das

Xirgo

2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

View full text Add to dashboard Cite

Control of systems of automated guided vehicles involves action planning at many levels. For efficient control of these systems, accurate estimation of cost parameters (speed, energy, task completion performance, et cetera is required. These parameters change along time, particularly in battery-operated robots, which are very sensitive to battery level variations. This work addresses the problem of on-line cost parameter identification and estimation for proper control decisions of the individual mobile robots and for the system as a whole. Several filtering and estimation methods have been investigated with respect to travelling times, which are dramatically affected by battery charges and condition of facility's floors, among other factors. Results show that these parameters depend on the robot, the route and the moment, so they are linked to a particular robot, a region of the floor and a time period (or to a battery level). Moreover, differences with static, pre-runtime travelling time computations, either heuristically or by characterization of real robots, are large enough to affect to system's performance and overall productivity and efficiency.

show abstract

“…Thus, improper tuning of noise covariance may lead to divergence of the filter over time, resulting in instability of the entire system [12]. Alternate algorithms that are relatively straightforward but computationally intensive have the advantage of accommodating noise model other than Gaussian such as Monte Carlo localization [13][14][15], FastSLAM [16], occupancy grid method [17] and unscented Kalman filter (UKF) [18] were also proposed.…”

Section: Introductionmentioning

confidence: 99%

Implementation of extended Kalman filter-based simultaneous localization and mapping: a point feature approach

2017

View full text Add to dashboard Cite

The implementation of extended Kalman filter-based simultaneous localization and mapping is challenging as the associated system state and covariance matrices along with the memory requirements become significantly large as the information space increases. Unique and consistent point features representing a segment of the map would be an optimal choice to control the size of covariance matrix and maximize the operating speed in a real-time scenario. A two-wheel differential drive mobile robot equipped with a Laser Range Finder with 0.02 m resolution was used for the implementation. Unique point features from the environment were extracted through an elegant line fitting algorithm, namely split only technique. Finally, the implementation showed remarkably good results with a success rate of 98% in feature identification and ±0.08 to ±0.11 m deviation in the generated map.

show abstract

Extended and Unscented Kalman Filters for mobile robot localization and environment reconstruction

Cited by 10 publications

References 14 publications

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

A study of time-varying cost parameter estimation methods in automated transportation systems based on mobile robots

Implementation of extended Kalman filter-based simultaneous localization and mapping: a point feature approach

Contact Info

Product

Resources

About