Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

Yi, Yingmin; Huang, Ying

doi:10.2478/cait-2014-0035

Cited by 2 publications

(2 citation statements)

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“…Simultaneous Localization And Mapping (SLAM) methods map the unknown surrounding space and ensure that the movement follows the built path [3]. SLAM presupposes that the mobile robot gathers data on the geometry of the surrounding space and defines the shift vector and rotation angle of the robot between separate time points [4]. Mathematically, this problem becomes the problem of defining the parameters of similarity transformations between two samples of two-dimensional geometrical measurements.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Aldoshkin

Tsarev

2018

Cybernetics and Information Technologies

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This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Aldoshkin

Tsarev

2018

Cybernetics and Information Technologies

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“…For demonstrating our method's universality, we set up a static environment to archive the requirement of Gmapping to show that DKDR can be simply applied to the existing common SLAM algorithm. If the robot is operating a task under noisier conditions which makes the data association difficult, such as occlusions of features or existence of dynamic features, some existing methods can be applied to deal with these critical situations, such as a robust data association method for noisy and dynamic environment [26], JPDA (Joint Probabilistic Data Association) [27], and LSDA (Landmark Sequence Data Association) [28]. Since the DKDR just needs associated features to judge the kidnapping, these data association methods can be applied before executing the DKDR under noisy conditions.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

Kidnapping Detection and Recognition in Previous Unknown Environment

Tian

2017

Journal of Sensors

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An unaware event referred to as kidnapping makes the estimation result of localization incorrect. In a previous unknown environment, incorrect localization result causes incorrect mapping result in Simultaneous Localization and Mapping (SLAM) by kidnapping. In this situation, the explored area and unexplored area are divided to make the kidnapping recovery difficult. To provide sufficient information on kidnapping, a framework to judge whether kidnapping has occurred and to identify the type of kidnapping with filter-based SLAM is proposed. The framework is called double kidnapping detection and recognition (DKDR) by performing two checks before and after the "update" process with different metrics in real time. To explain one of the principles of DKDR, we describe a property of filter-based SLAM that corrects the mapping result of the environment using the current observations after the "update" process. Two classical filter-based SLAM algorithms, Extend Kalman Filter (EKF) SLAM and Particle Filter (PF) SLAM, are modified to show that DKDR can be simply and widely applied in existing filter-based SLAM algorithms. Furthermore, a technique to determine the adapted thresholds of metrics in real time without previous data is presented. Both simulated and experimental results demonstrate the validity and accuracy of the proposed method.

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Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

Cited by 2 publications

References 15 publications

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Kidnapping Detection and Recognition in Previous Unknown Environment

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