Onboard dynamic RGB‐D simultaneous localization and mapping for mobile robot navigation

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

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Mobile robot navigation based on EWA with adaption of particle filter and map merging algorithms for localization and mapping

International Journal of Precision Engineering and Manufacturing ◽

10.1007/s12541-011-0058-z ◽

2011 ◽

Vol 12 (3) ◽

pp. 451-459 ◽

Cited By ~ 4

Author(s):

Dilshat Saitov ◽

Suk Gyu Lee

Keyword(s):

Mobile Robot ◽

Particle Filter ◽

Robot Navigation ◽

Mobile Robot Navigation ◽

Localization And Mapping ◽

Merging Algorithms ◽

Map Merging

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Implementation and Reconfiguration of Robot Operating System on Human Follower Transporter Robot

CommIT (Communication and Information Technology) Journal ◽

10.21512/commit.v9i2.1646 ◽

2015 ◽

Vol 9 (2) ◽

pp. 59

Author(s):

Addythia Saphala ◽

Prianggada Indra Tanaya

Keyword(s):

Operating System ◽

Mobile Robot ◽

Navigation System ◽

Robot Navigation ◽

Simultaneous Localization And Mapping ◽

Kinect Sensor ◽

Operation System ◽

Robot Operating System ◽

Localization And Mapping

Robotic Operation System (ROS) is an im- portant platform to develop robot applications. One area of applications is for development of a Human Follower Transporter Robot (HFTR), which can be considered as a custom mobile robot utilizing differential driver steering method and equipped with Kinect sensor. This study discusses the development of the robot navigation system by implementing Simultaneous Localization and Mapping (SLAM).

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Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419874645 ◽

2019 ◽

Vol 16 (5) ◽

pp. 172988141987464 ◽

Cited By ~ 1

Author(s):

Cong Hung Do ◽

Huei-Yung Lin

Keyword(s):

Kalman Filter ◽

Mobile Robot ◽

Extended Kalman Filter ◽

Optimization Technique ◽

Computational Cost ◽

Simultaneous Localization And Mapping ◽

Mobile Robot Navigation ◽

Filter Technique ◽

Localization And Mapping ◽

Neuro Fuzzy

Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline using a particle swarm optimization technique. With this approach, the advantages of high-dimensional search space can be exploited and more effective training can be achieved. In the experiments, the mobile robot navigation with a number of landmarks under two benchmark situations is evaluated. The results have demonstrated that the proposed adaptive neuro-fuzzy extended Kalman filter technique provides the improvement in both performance efficiency and computational cost.

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