Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

Tørdal, Sondre Sanden; Hovland, Geir

doi:10.4173/mic.2017.2.3

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…While ARTag tracking is relatively robust and accurate, the plane of the marker must generally be facing the camera, and the range of viewing angles from which the ARTag can be detected is limited, since the visibility of the flat pattern diminishes when observed from a sharp angle. One common way to circumvent the view-angle limitation is to place multiple different ARTags on the sides of a rigid 3D object, for example multiple ARTags on each side of a cube [22,23,24], which enables the camera to see at least one marker at any orientation. Unfortunately, these complex markers featuring multiple ARTags are large and thus their use in animal behavioral tracking is limited.…”

Section: Object Tracking Using Model-based Approachesmentioning

confidence: 99%

Wide-Angle, Monocular Head Tracking using Passive Markers

Vágvölgyi

Jayakumar

Madhav

et al. 2021

Preprint

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Camera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. We present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal's head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360 degrees in yaw, and approximately +/-120 degrees in roll and pitch). Experiments with live animals demonstrate that the system can reliably identifyrat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. Our solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion. The proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.

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Section: Object Tracking Using Model-based Approachesmentioning

confidence: 99%

Wide-Angle, Monocular Head Tracking using Passive Markers

Vágvölgyi

Jayakumar

Madhav

et al. 2021

Preprint

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“…A matriz dos quatérnios médiosq, que é dado por v em (3) correspondente ao maior valor do auto-valor λ [11].…”

Section: Posição E Orientação Utilizando Um Marco Fiducialunclassified

Indoor localization for mobile robots using fiducial markers

Cardoso¹,

Reis²,

Santos³

2020

Proceedings of the III Brazilian Humanoid Robot Workshop (BRAHUR) and IV Brazilian Workshop on Service Robotics (BRASERO)

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The work aimed to use fiducial markers to locate a robot indoors using a simulation environment as a testing tool. An environment with scattered fiducial markers was developed for the robot to identify and be able to recalculate its position with the odometer sensors reference frame. It was observed that this method applied for indoor environments has a low computational cost, caused for the readability of the landmarks, and a high accuracy in robot localization compared to other methods.

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“…With error detection and correction, the four corners of the identified marker can be used as reference points to estimate the camera pose by solving the Perspective-n-Point (PnP) problem [28,29,30,31,32,33], given that the camera is properly calibrated. Amongst these fiducial markers proposed in recent papers, ArUco markers, presented by Garrido-Jurado et al, have gained popularity in visual servo systems [34]. By using the ArUco library supported by OpenCV, it is not difficult to generate configurable dictionaries of markers and make a C++ program capable of identifying and localizing those markers within the predefined dictionary.…”

Section: Introductionmentioning

confidence: 99%

An Onboard Vision-Based System for Autonomous Landing of a Low-Cost Quadrotor on a Novel Landing Pad

Liu

Zhang

Tian

et al. 2019

Sensors

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In this paper, an onboard vision-based system for the autonomous landing of a low-cost quadrotor is presented. A novel landing pad with different optical markers sizes is carefully designed to be robustly recognized at different distances. To provide reliable pose information in a GPS (Global Positioning System)-denied environment, a vision algorithm for real-time landing pad recognition and pose estimation is implemented. The dynamic model of the quadrotor is established and a system scheme for autonomous landing control is presented. A series of autonomous flights have been successfully performed, and a video of the experiment is available online. The efficiency and accuracy of the presented vision-based system is demonstrated by using its position and attitude estimates as control inputs for the autonomous landing of a self-customized quadrotor.

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Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

Cited by 14 publications

References 16 publications

Wide-Angle, Monocular Head Tracking using Passive Markers

Wide-Angle, Monocular Head Tracking using Passive Markers

Indoor localization for mobile robots using fiducial markers

An Onboard Vision-Based System for Autonomous Landing of a Low-Cost Quadrotor on a Novel Landing Pad

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