First-person indoor navigation via vision-inertial data fusion

Farnoosh, Amirreza; Nabian, Mohsen; Closas, Pau; Ostadabbas, Sarah

doi:10.1109/plans.2018.8373507

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…A comparative study of IMU and camera based localization can be seen in [102]. Most recent work on hybrid localization is discussed in [103,104,105,106,107,108,109]. Among these studies, the hybrid indoor localization can be used for better performance and which reduces IMU sensor and camera position errors.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

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Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

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Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

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“…This sampling rate is considered fast enough for human's visual inspection. However, in case some applications such as automatic activity recognition may require higher sampling rate, the original angle can be synchronized with the video starting from sample C (17).…”

Section: Synchronization Of Video and Imu Datamentioning

confidence: 99%

“…The accuracy of activity recognition when camera and IMU were fused was about 10% higher than that of using camera or IMU alone. Farnoosh et al [17] fused inertial data and video recorded from a smartphone for indoor navigation. The inertial sensor was used to estimate the smartphone orientation, and the navigation accuracy was improved compared to navigation without orientation estimation.…”

mentioning

confidence: 99%

Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

2019

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As walking assessment is commonly done through visual inspection, it is beneficial to make joint angle information available to the clinicians for better assessment. The main challenge is that the video camera and inertial sensor are usually two separate systems, and the recordings are hard to be initialized at the same time manually. This creates a problem that the inertial sensor data is not temporally synchronized with the video camera. This paper proposes a method to synchronize the video and sensor data by detecting and matching the maximum backward swings of the leg. The proposed method is validated by blinking LED and transmitting LED flag to the computer at the same time. The synchronization error of the proposed method is low at about 0±2 frames compared to the validation method.

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“…A more general insight into this process for extracting information available in the relationships between multiple data streams could short circuit this trial-and-error process to underpin progress across numerous domains and is, therefore, urgently needed. While the commonality of the underlying mathematics has been widely discussed (e.g., [2]), the authors propose that forward progress requires establishing a common scientific framework for multimodal data fusion (MMDF) to enable apples-to-apples comparisons of solutions developed for applications as diverse as first-person indoor navigation [3], data-driven decision support systems for assisting medical diagnosis [4], real-time water quality anomaly detection [5], and production line optimization [6]. Such a framework would enable the research community to integrate lessons learned from MMDF efforts across domains-domains which, despite the varying deliverables, use data with similar characteristics (varying dimensionalities, resolutions, noise/uncertainty patterns, gaps, etc.).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Data Fusion-Moving From Domain-Specific Algorithms to Transdomain Understanding for Accelerated Solution Development

et al. 2019

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The exploding availability of new data streams now enables insights to be garnered through the integration (fusion) of multiple data sources (modalities); however, currently, it remains difficult to predict a priori which multimodal data fusion (MMDF) methods and architectures will be best suited for a novel application, leading to trial-and-error approaches that are inefficient in both time and cost. Although MMDF strategies are being applied ad hoc in many different fields (e.g., healthcare, autonomous navigation, robotics, manufacturing, transportation, etc.), results are reported in different journals using different terminology for different target audiences. These applications have diverse needs (assessment, prediction, detection, etc.), resources (number and quality of available data streams), and constraints (social, economic, and physics); however, they share many properties when these lists are evaluated at the metalevel. The authors propose that a shared MMDF framework is urgently needed to allow aggregation of insights across applications, with the end goal of developing an MMDF recommendation tool that overcomes the need for trial-and-error approaches to expedite implementation of new solutions across a wide range of disciplines. To provide context for this Special Issue and provide an overview of key related issues, this editorial presents an abbreviated summary of the current state of MMDF usage from multiple perspectives, based on input from a March 2018 workshop, and finally presents four key research priorities for the field of MMDF.

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First-person indoor navigation via vision-inertial data fusion

Cited by 4 publications

References 24 publications

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Automatic Synchronization of Markerless Video and Wearable Sensors for Walking Assessment

Multimodal Data Fusion-Moving From Domain-Specific Algorithms to Transdomain Understanding for Accelerated Solution Development

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