Landmark-Based Particle Localization Algorithm for Mobile Robots With a Fish-Eye Vision System

Han, Seung-Beom; Kim, Jong-Hwan; Myung, Hyun

doi:10.1109/tmech.2012.2213263

Cited by 38 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The distance and angle between robots is given in

\begin{matrix} right ρ_{12} & left = \sqrt{{(x_{2} - x_{1})}^{2} + {(y_{2} - y_{1})}^{2}} = \sqrt{{(Δ x)}^{2} + {(Δ y)}^{2}} & right \\ right ψ & left = φ_{12} + θ_{1} = atan 2 (Δ y, Δ x) \end{matrix}

This model can be used directly in the fusion scheme (using linearization) or solved according to the number of R i , D r and the received M r . When detecting more than one R i , similar techniques to landmark triangulation can be used to obtain the robot pose from the relative information. The worst case presents when detecting only one R i , as D i may be different.…”

Section: Models For the Multirobot Localizationmentioning

confidence: 99%

Event based distributed kalman filter for limited resource multirobot cooperative localization

Marín

Soriano

Vallés

et al. 2019

Asian Journal of Control

View full text Add to dashboard Cite

This paper presents a multirobot cooperative event based localization scheme with improved bandwidth usage in a heterogeneous group of mobile robots. The proposed method relies on an agent based framework that defines the communications between robots and on an event based Extended Kalman Filter that performs the cooperative sensor fusion from local, global and relative sources.The event is generated when the pose error covariance exceeds a predefined limit. By this, the robots update the pose using the relative information available only when necessary, using less bandwidth and computational resources when compared to the time based methods, allowing bandwidth allocation for other tasks while extending battery life. The method is tested using a simulation platform developed in the programming language JAVA with a group of differential mobile robots represented by an agent in a JADE framework. The pose estimation performance, error covariance and number of messages exchanged in the communication are measured and used to compare the traditional time based approach with the proposed event based algorithm. Also, the compromise between the accuracy of the localization method and the bandwidth usage is analyzed for different event limits. A final experimental test with two SUMMIT XL robots is shown to validate the simulation results. KEYWORDScooperative localization, cooperative sensor fusion, distributed kalman filtering, event based communication, event based estimation, mobile robots ities, which can produce great uncertainty in the pose estimation if not taken into account.A single robot can estimate its pose using sensors with Local information (encoders, gyroscopes, etc.) via Dead Reckoning [1] with an unbounded estimation error growth over time [2], or with Global information (GPS, zenithal camera, etc.) with direct pose measurement but slow response time and limited environment applicability

show abstract

“…The distance and angle between robots is given in

\begin{matrix} right ρ_{12} & left = \sqrt{{(x_{2} - x_{1})}^{2} + {(y_{2} - y_{1})}^{2}} = \sqrt{{(Δ x)}^{2} + {(Δ y)}^{2}} & right \\ right ψ & left = φ_{12} + θ_{1} = atan 2 (Δ y, Δ x) \end{matrix}

Section: Models For the Multirobot Localizationmentioning

confidence: 99%

Event based distributed kalman filter for limited resource multirobot cooperative localization

Marín

Soriano

Vallés

et al. 2019

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Because of this limitation, visual odometry, which is not much affected by platform types, has become more important than the classical encoder odometry. Actually, in robotics and computer vision, many techniques and algorithms have been developed for 3-D mapping and visual odometry using monocular cameras [8,9], fish-eye vision sensors [10], stereo cameras [11] and RGB-D sensors [12,13].…”

Section: Introductionmentioning

confidence: 99%

Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment

Kim

Han

Kim

2015

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

Abstract. This paper proposes a robust visual odometry algorithm using a Kinect-style RGB-D sensor and inertial measurement unit (IMU) in a highly dynamic environment. Based on SURF (Speed Up Robust Features) descriptor, the proposed algorithm generates 3-D feature points incorporating depth information into RGB color information. By using an IMU, the generated 3-D feature points are rotated in order to have the same rigid body rotation component between two consecutive images. Before calculating the rigid body transformation matrix between the successive images from the RGB-D sensor, the generated 3-D feature points are filtered into dynamic or static feature points using motion vectors. Using the static feature points, the rigid body transformation matrix is finally computed by RANSAC (RANdom SAmple Consensus) algorithm. The experiments demonstrate that visual odometry is successfully obtained for a subject and a mobile robot by the proposed algorithm in a highly dynamic environment. The comparative study between proposed method and conventional visual odometry algorithm clearly show the reliability of the approach for computing visual odometry in a highly dynamic environment.

show abstract

“…It is a challenging research topic in mobile robot which has received much attention [1]. Moving around without location for robots is the same as walking with closed eyes for people [2]. They do not know where they are and which direction they should move to.…”

Section: Introductionmentioning

confidence: 99%

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Shang

Wen

Wang

et al. 2014

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

We present a detailed study on the RSS-based location techniques in wireless sensor networks (WSN). There are two aspects in this paper. On the one hand, the accurate RSSI received from nodes is the premise of accurate location. Firstly, the distribution trend of RSSI is analyzed in this experiment and determined the loss model of signal propagation by processing experimental data. Secondly, in order to determine the distance between receiving nodes and sending nodes, Gaussian fitting is used to process specific RSSI at different distance. Moreover, the piecewise linear interpolation is introduced to calculate the distance of any RSSI. On the other hand, firstly, the RSSI vector similarity degree (R-VSD) is used to choose anchor nodes. Secondly, we designed a new localization algorithm which is based on the quadrilateral location unit by using more accurate RSSI and range. Particularly, there are two localization mechanisms in our study. In addition, the generalized inverse is introduced to solve the coordinates of nodes. At last, location error of the new algorithm is about 17.6% by simulation experiment.

show abstract

Landmark-Based Particle Localization Algorithm for Mobile Robots With a Fish-Eye Vision System

Cited by 38 publications

References 22 publications

Event based distributed kalman filter for limited resource multirobot cooperative localization

Event based distributed kalman filter for limited resource multirobot cooperative localization

Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment

A Location Estimation Algorithm Based on RSSI Vector Similarity Degree

Contact Info

Product

Resources

About