The artificial landmark design for mobile robots localization and mapping

Huletski, Arthur; Kartashov, Dmitriy; Krinkin, Kirill

doi:10.1109/fruct.2015.7117971

Cited by 3 publications

(1 citation statement)

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“…For landmark based localization, which can be either artificial or natural, the usual technique is done by means of identification and recognition of objects (landmarks) in the environment and posterior calculation of the robot’s pose with respect to the found object. Artificial landmarks such as Quick Response codes (QR-codes) have been being used recently in mobile robot landmark localization [ 13 , 14 ]. Specific designed patterns are also used in artificial landmark localization [ 15 ] in order to attend specific purposes or to overcome specific problems, such as the ones found in industry [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization

Souto

Castro

Gonçalves

et al. 2017

Sensors

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Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the Sl0 algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach.

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