Localisation and position correction for mobile robot using artificial visual landmarks

In this paper, we proposed a visually stimulated motor control (VSMC) system\ud for autonomous navigation of mobile robots. Inspired from a locusts’ motion sensitive\ud interneuron – lobula giant movement detector (LGMD), the presented VSMC system enables a\ud robot exploring local paths or interacting with dynamic objects effectively using visual input\ud only. The VSMC consists of a pair of LGMD visual neural networks and a simple motor\ud command generator. Each LGMD processes images covering part of the wide field of view and\ud extracts relevant visual cues. The outputs from the two LGMDs are compared and interpreted\ud into executable motor commands directly. These motor commands are then executed by the\ud robot’s wheel control system in real-time to generate corresponded motion adjustment\ud accordingly. Our experiments showed that this bio-inspired VSMC system worked well in\ud different scenarios

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“…(2010) and its application to other areas such as in Deng et.al. (2010) and Kroumov and Okuyama (2012).…”

Section: Discussionmentioning

confidence: 98%

Visually stimulated motor control for a robot with a pair of LGMD visual neural networks

Yue

Rind

2012

IJAMECHS

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“…If the robot is not in the desired position, it will perform a correcting position. Okuyama et al [21] used an artificial landmark as a QR code placed on the wall and used it as the correction position. Position correction can use lines as the simpler artificial landmark.…”

Section: Introductionmentioning

confidence: 99%

Position correction of mobile robot by detecting line landmark using Hough line transform

Anggoro,

Setiawan,

Nurhayati

2023

J. Phys.: Conf. Ser.

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Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.

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“…Using artificial landmarks to improve and correct UAV positioning is also a popular and efficient method. QR-code was used to calibrate the position of a mobile robot navigated via vSLAM in [31], and the proposed method was verified to improve the robot positioning when vSLAM is implemented. However, this method must ensure that the camera faces the artificial landmark perpendicularly to detect it.…”

Section: Introductionmentioning

confidence: 99%

Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment

Lee

Chen

Shen

et al. 2022

Sensors

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With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual–inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

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Localisation and position correction for mobile robot using artificial visual landmarks

Cited by 5 publications

References 9 publications

Visually stimulated motor control for a robot with a pair of LGMD visual neural networks

Visually stimulated motor control for a robot with a pair of LGMD visual neural networks

Position correction of mobile robot by detecting line landmark using Hough line transform

Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment

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