Three Tiered Visual-Inertial Tracking and Mapping for Augmented Reality in Urban Settings

Calloway, Thomas; Megherbi, Dalila B.

doi:10.1109/civemsa48639.2020.9132969

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…With the rapid development of computer vision, the VSLAM (Visual SLAM) system with the help of cameras has begun to become the mainstream of research by various teams due to its convenient use and low cost. The VSLAM system has been well applied in the fields of augmented reality (Calloway and Megherbi, 2020), driverless driving (Nguyen et al, 2018), and robotics (Liu, 2021). Virtual objects registered with VSLAM technology have better stability and accuracy in today's popular augmented reality applications.…”

Section: Introductionmentioning

confidence: 99%

VSLAM method based on object detection in dynamic environments

Liu

Chen

et al. 2022

Front. Neurorobot.

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Augmented Reality Registration field now requires improved SLAM systems to adapt to more complex and highly dynamic environments. The commonly used VSLAM algorithm has problems such as excessive pose estimation errors and easy loss of camera tracking in dynamic scenes. To solve these problems, we propose a real-time tracking and mapping method based on GMM combined with YOLOv3. The method utilizes the ORB-SLAM2 system framework and improves its tracking thread. It combines the affine transformation matrix to correct the front and back frames, and employs GMM to model the background image and segment the foreground dynamic region. Then, the obtained dynamic region is sent to the YOLO detector to find the possible dynamic target. It uses the improved Kalman filter algorithm to predict and track the detected dynamic objects in the tracking stage. Before building a map, the method filters the feature points detected in the current frame and eliminates dynamic feature points. Finally, we validate the proposed method using the TUM dataset and conduct real-time Augmented Reality Registration experiments in a dynamic environment. The results show that the method proposed in this paper is more robust under dynamic datasets and can register virtual objects stably and in real time.

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Section: Introductionmentioning

confidence: 99%

VSLAM method based on object detection in dynamic environments

Liu

Chen

et al. 2022

Front. Neurorobot.

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“…The aim of the current work is to provide the most accurate orientation estimation during this lapse of time. Many applications, such as high dynamic motion by aerial robots [1] or augmented reality equipment [2] require virtually no delay between real motions and their estimations. Visual odometry algorithms add a delay of several tens of milliseconds.…”

Section: Introductionmentioning

confidence: 99%

Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce Systematic Error

Faizullin¹,

Ferrer²

2021

Preprint

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In this paper, we propose an algorithm to combine multiple cheap Inertial Measurement Unit (IMU) sensors to calculate 3D-orientations accurately. Our approach takes into account the inherent and non-negligible systematic error in the gyroscope model and provides a solution based on the error observed during previous instants of time. Our algorithm, the Best Axes Composition (BAC), chooses dynamically the most fitted axes among IMUs to improve the estimation performance. We compare our approach with a probabilistic Multiple IMU (MIMU) approach, and we validate our algorithm in our collected dataset. As a result, it only takes as few as 2 IMUs to significantly improve accuracy, while other MIMU approaches need a higher number of sensors to achieve the same results.

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A new spatial spherical pattern model into interactive cartography pattern: multi-dimensional data via geostrategic cluster

Zerdoumi

Hashem

Jhanjhi

2022

Multimed Tools Appl

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A growing amount of research conducted in digital, cooperative with advances in Artificial Intelligence, Computer Vision including Machine learning, has managed to the advance of progressive techniques that aim to detect and process affective information contained in multi-modal evidences. This research intends to bring together for theoreticians and practitioners from academic fields, professionals and industries and extends to be visualizing cries such epidemic, votes, social Phenomena in spherical representation interactive model working in the broad range of topics relevant to multi - modal data processing and forensics tools developing. Furthermore, progress has been made in this research besides that in this research conducted progression of mapping claims in present epoch necessitate the capacities of virtual guide of any understandable Geo-Visualization of spatial features that talented to convert the quantities of spatial pattern into cartography. The enlargement of a novel approaches fit for visualization of spatial pattern constituencies Starting exclusive Input Set of object O, set associated with feature F for regenerating Output the set C , interested region I special target C Even so, as indicated by the construction of the prototype as listed earlier in this thread, does it have the incentive for improvements: Representation could be used by Google Earth can Using Project enhancement representation whereby provides a 3D or 4D interaction with life measures with a view to cartography. In addition, the initiative suggests that a tool not accessible for disseminating information to the public can be addressed by the use of online mapping, which fuses with trends visualization for political circles and electors. But as mentioned above the framework is developed and it's also possible in the current example, for improvements: The project's representation 3D or 4D interacting Earth can use measures of life Earth From the map viewpoint. That's what that says. That means that. Which just means. Developers have concerns that. So it. Designers concern about that. This study supports the new, multi - demission and deployed countries in conjunction with another data is processed. Comprehensive, well-interpreted source data for the Data like Malaysia Jabatan Pendaftaran (JPN).

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Three Tiered Visual-Inertial Tracking and Mapping for Augmented Reality in Urban Settings

Cited by 5 publications

References 15 publications

VSLAM method based on object detection in dynamic environments

VSLAM method based on object detection in dynamic environments

Best Axes Composition: Multiple Gyroscopes IMU Sensor Fusion to Reduce Systematic Error

A new spatial spherical pattern model into interactive cartography pattern: multi-dimensional data via geostrategic cluster

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