Automatic Reconstruction of Wide-Area Fiducial Marker Models

Klopschitz, Manfred; Schmalstieg, Dieter

doi:10.1109/ismar.2007.4538828

Cited by 21 publications

(8 citation statements)

References 20 publications

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“…A similar approach is presented in [30] for an autonomous blimp. Later, Klopschitz et al [31] proposed a method where Structure from Motion was first employed to reconstruct the scene, and then the markers were located from the recovered camera poses. Karam et al [32] presented a method to create a map of markers as a pose graph where nodes represent markers and edge the relative pose between them.…”

Section: Related Workmentioning

confidence: 99%

UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Muñoz-Salinas

Medina-Carnicer

2020

Pattern Recognition

110

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This paper proposes a novel approach for Simultaneous Localization and Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches use natural landmarks (such as keypoints). However, they are unstable over time, repetitive in many cases or insufficient for a robust tracking (e.g. in indoor buildings). On the other hand, other approaches have employed artificial landmarks (such as squared fiducial markers) placed in the environment to help tracking and relocalization. We propose a method that integrates both approaches in order to achieve long-term robust tracking in many scenarios.Our method has been compared to the start-of-the-art methods ORB-SLAM2 [1] and LDSO [2] in the public dataset Kitti [3], Euroc-MAV [4], TUM [5] and SPM [6], obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.

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Section: Related Workmentioning

confidence: 99%

UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Muñoz-Salinas

Medina-Carnicer

2020

Pattern Recognition

110

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“…An alternative to tracking natural keypoints in images is detecting fiducial markers. Klopschitz and other authors [ 59 , 60 , 61 ] introduced methods for solving visual SLAM based on markers. However, they do not consider optimizing the estimated marker poses for the ambiguity problem.…”

Section: Background and Related Workmentioning

confidence: 99%

Smart Artificial Markers for Accurate Visual Mapping and Localization

Ortiz-Fernandez

Cabrera-Avila

Silva

et al. 2021

Sensors

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Artificial marker mapping is a useful tool for fast camera localization estimation with a certain degree of accuracy in large indoor and outdoor environments. Nonetheless, the level of accuracy can still be enhanced to allow the creation of applications such as the new Visual Odometry and SLAM datasets, low-cost systems for robot detection and tracking, and pose estimation. In this work, we propose to improve the accuracy of map construction using artificial markers (mapping method) and camera localization within this map (localization method) by introducing a new type of artificial marker that we call the smart marker. A smart marker consists of a square fiducial planar marker and a pose measurement system (PMS) unit. With a set of smart markers distributed throughout the environment, the proposed mapping method estimates the markers’ poses from a set of calibrated images and orientation/distance measurements gathered from the PMS unit. After this, the proposed localization method can localize a monocular camera with the correct scale, directly benefiting from the improved accuracy of the mapping method. We conducted several experiments to evaluate the accuracy of the proposed methods. The results show that our approach decreases the Relative Positioning Error (RPE) by 85% in the mapping stage and Absolute Trajectory Error (ATE) by 50% for the camera localization stage in comparison with the state-of-the-art methods present in the literature.

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“…Therefore, the registration may be invalidated under the circumstances of large changes in illumination and viewpoint. The similar problems can be seen in [16-19]. Most recently, [20] gives an augmented optical flow tracker based system which is most notable for the evident high-quality patch tracking.…”

Section: Related Researchesmentioning

confidence: 61%

Registration Combining Wide and Narrow Baseline Feature Tracking Techniques for Markerless AR Systems

Duan

Guan

Yang

2009

Sensors

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Augmented reality (AR) is a field of computer research which deals with the combination of real world and computer generated data. Registration is one of the most difficult problems currently limiting the usability of AR systems. In this paper, we propose a novel natural feature tracking based registration method for AR applications. The proposed method has following advantages: (1) it is simple and efficient, as no man-made markers are needed for both indoor and outdoor AR applications; moreover, it can work with arbitrary geometric shapes including planar, near planar and non planar structures which really enhance the usability of AR systems. (2) Thanks to the reduced SIFT based augmented optical flow tracker, the virtual scene can still be augmented on the specified areas even under the circumstances of occlusion and large changes in viewpoint during the entire process. (3) It is easy to use, because the adaptive classification tree based matching strategy can give us fast and accurate initialization, even when the initial camera is different from the reference image to a large degree. Experimental evaluations validate the performance of the proposed method for online pose tracking and augmentation.

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Automatic Reconstruction of Wide-Area Fiducial Marker Models

Cited by 21 publications

References 20 publications

UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Smart Artificial Markers for Accurate Visual Mapping and Localization

Registration Combining Wide and Narrow Baseline Feature Tracking Techniques for Markerless AR Systems

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