An Improved Tracking Using IMU and Vision Fusion for Mobile Augmented Reality Applications

Kumar, Kriti; Varghese, Ashley; Reddy, P. Siva Kota; N, Narendra; Swamy, Prashanth; Chandra, M. Girish; Balamuralidhar, P.

doi:10.5121/ijma.2014.6502

Cited by 12 publications

(13 citation statements)

References 30 publications

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“… Camera frame {c}: This frame is attached to the camera on the mobile robot with the x-axis pointing to the image plane in the right direction and z-axis pointing along the optical axis and origin located at the camera optical center. The IMU method provides orientation of the body {b} with respect to (wrt) world frame {w} R wb and vision method provides orientation of the object {o} wrt to camera frame {c} R co [ 26 , 60 ]. …”

Section: Proposed Modeling Methodsmentioning

confidence: 99%

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Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

172

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Section: Proposed Modeling Methodsmentioning

confidence: 99%

“…Pose estimation has been studied in past and recent times for applications in object positioning [ 7 ], robotics, and augmented reality (AR) tracking [ 26 ]. This section will discuss the existing technologies used for pose estimation in our environment these days.…”

Section: Related Workmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

172

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“…In [11], two sensor fusion methods based on EKF were developed to estimate the pose of the monocular camera with the IMU sensor. One report of the literature [3] describes a robust tracking system that combines the IMU sensor and camera for Mobile Augmented Reality (MAR) applications. A relative position estimator for AUVs [6] was developed by fusing a monocular camera with an accelerometer and a rate gyrosensor based on EKF.…”

Section: Related Workmentioning

confidence: 99%

“…Integrating multi-sensor data based on the benefits of each sensor can achieve more wide range, accurate, rapid, and robust measurement than measurement by a single sensor. In AR research, sensor fusion based on vision sensing is used to track objects correctly in an real image sequence and to overlay virtual images on it (e.g., [1], [2] [3]). In the area of human measurement, vision-based sensor fusion is used to measure human body motion [4].…”

Section: Introductionmentioning

confidence: 99%

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Robust object tracking for underwater robots by integrating stereo vision, inertial and magnetic sensors

Sakagami

Choi

2016

Stochastic Systems Theory and its Applications (SSS)

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Abstract-As described in this paper, we propose a robust tracking system with a stereo vision, IMU, and magnetic sensor for underwater robots to estimate the position of a stationary object even when the object is out of the field of view (FOV) of a camera or occlusion occurs. During maneuvering of underwater robots, they are subject to external disturbances. Their cameras are always moving. Therefore, objects in an image move out of the FOV of a camera unexpectedly.As described in this paper, we integrate an IMU and a magnetic sensor with a stereo vision sensor to estimate an object's position robustly. In the process of the estimation, Kalman filters are used to estimate the sensor system orientation. First, we conduct numerical analyses to investigate the estimation accuracy of the proposed method against sensor noise. Then, a preliminary experiment is conducted to demonstrate the robustness of the proposed method against occlusion and moving out of the FOV. As a first step, we examine the tracking performance with regard to rotational motion of the sensor system.

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A Wireless, Modular and Wearable System for the Recognition and Assessment of Foot Drop Pathology

Noriega

Rojas

Murrugarra

2019

Communications in Computer and Information Science

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An Improved Tracking Using IMU and Vision Fusion for Mobile Augmented Reality Applications

Abstract: Mobile Augmented Reality (MAR) is becoming an important cyber-physical

Cited by 12 publications

References 30 publications

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Robust object tracking for underwater robots by integrating stereo vision, inertial and magnetic sensors

A Wireless, Modular and Wearable System for the Recognition and Assessment of Foot Drop Pathology

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