Simultaneous Localization and Mapping: A Pseudolinear Kalman Filter (PLKF) Approach

Pathiranage, Chandima Dedduwa; Watanabe, Keigo; Jayasekara, A. G. Buddhika P.; Izumi, Kiyotaka

doi:10.1109/iciafs.2008.4783921

Cited by 7 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are generally three major approaches studied for SLAM, optimal control approach, local submap approach, and frontier based approach, to achieve the localization and mapping purposes in an active and intelligent way. Similar work on Kalman filter (KF) based SLAM has been addressed extensively in the literature [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…Second, this approach could be unavailable when the process and sensor noise covariance matrices are inaccurate. To improve the accuracy and fast convergence of state estimation involved in KF, the pseudolinear model based Kalman filter (PLKF) based SLAM is proposed [17]. The PLKF based SLAM outperforms the conventional EKF based SLAM since the pseudolinear model preserves the nonlinearity in the system, motion, and observation models.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Zhou

Tian

et al. 2015

Mobile Information Systems

View full text Add to dashboard Cite

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Mobile Information Systemsmentioning

confidence: 99%

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Zhou

Tian

et al. 2015

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…At a theoretical and conceptual level, SLAM can now be considered a solved problem. However, substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm (Chanier et al, 2008) (Pathiranage et al, 2008). The first problem is the computational complexity due to the growing state vector with each added landmark in the environment.…”

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

Towards Semantically Intelligent Robots

Elçi¹,

Rahnama²,

Cyprus³

2009

Advances in Human-Robot Interaction

View full text Add to dashboard Cite

“…To overpass these issues, the majority of past work assumed a known initial condition for RL between two arbitrary robots [4]. In addition to EKF, particle filter (PF) [5] and unscented Kalman filter (UKF) [6], and pseudolinear Kalman filter [7], [8] have been also used to achieve RL in collaboration tasks.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear moving horizon state estimation for multi-robot relative localization

Mehrez

Mann

Gosine

2014

2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

This paper presents a novel approach for a multirobot system's relative localization (RL), where one or more robots are located and tracked with respect to another robot frame of reference. With a known initial estimate of a robot being tracked, the extended Kalman filter (EKF) has been shown to perform adequately well to achieve the RL. However, with an arbitrary initial estimate, EKF performance may become unstable and/or require a high number of iterations to achieve an acceptable tracking error. In this paper, moving horizon estimation (MHE) has been adopted to achieve the RL objective. Although MHE has been highlighted in the literature to be computationally intractable, in this work, an efficient algorithm based on Real Time Iteration (RTI) scheme has been exported using an automatic C code generation toolkit. The exported code is adapted for the RL problem and requires computational capacity of the order ones of milliseconds. The MHE performance is compared against the EKF in numerical simulations. Under arbitrary estimator initialization, the results confirms that MHE over performs EKF in terms of the number of iterations required for convergence while satisfying the real-time requirements.

show abstract

Simultaneous Localization and Mapping: A Pseudolinear Kalman Filter (PLKF) Approach

Cited by 7 publications

References 7 publications

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Towards Semantically Intelligent Robots

Nonlinear moving horizon state estimation for multi-robot relative localization

Contact Info

Product

Resources

About