Calibration-free Localization for Mobile Robots Using an External Stereo Camera

Yasuda, Shinya; Kumagai, Takashi; Yoshida, Hiroshi

doi:10.1109/icce46568.2020.9042969

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Zhang Y [10] localized the robot using the an ArUco marker attached on the robot through the external surveillance camera. Yasuda S et al [11] installed build-in LEDs on the top board of the robot, and used a stereo camera to locate the mobile robots by detecting the bright part of the optical images. However, the above methods need to change the original structure of the robot, and these marks are easily shielded by the dust in the working environment of the industrial robot.…”

Section: Introductionmentioning

confidence: 99%

Localization of Wall Climbing Robot on Cylinder-Shaped Steel

Zhang

Huang

Sun

2022

Advances in Transdisciplinary Engineering

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Due to the limitation of sensor application in special environment such as relatively closed and magnetic interference environment, the positioning and the heading angle errors of wall climbing robots accumulate with time. This paper proposes a difference projection localization method based on an external RGB-D camera and a robot-carried inertial measurement unit (IMU). We differential the depth image to obtain the distance change due to the robot occupancy. Then, the 3D point cloud information is converted into 2D image information by projecting the above distances along the normal vector of the robot chassis, which greatly speeds up the computational speed. The position of the robot is calculated by studying the statistical characteristics of the projection. Two EKFs are designed to estimate the attitude, taking the gravity vector and the normal vector of the robot chassis as observation. The experimental results show that the localization error of the wall climbing robot is within 0.017m, and the heading angle error of the attitude estimation is within 3.1∘. The obtained results prove its applicability in self-localization of the wall climbing robot.

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Section: Introductionmentioning

confidence: 99%