An Accuracy Generalization Benchmark for Wireless Indoor Localization based on IMU Sensor Data

Eyobu, Odongo Steven; Poulose, Alwin; Han, Dong Seog

doi:10.1109/icce-berlin.2018.8576213

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…The rotation matrix was already computed as an intermediate step of the position computation (7). Employing the inverse of the Rodrigues' formula, we convert the rotation matrix into the rotation vector of the camera in the world frame (8) as follows:…”

Section: Camera Pose Estimationmentioning

confidence: 99%

“…High resolution ground truth for indoor positioning is rare because of the difficulties in collecting accurate position data on every point the object of interest passes. This difficulty prevents the researchers from setting a generalized benchmark for indoor positioning systems [8]. The positioning community lacks a clear standard for evaluating positioning systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An indoor localization dataset and data collection framework with high precision position annotation

Daniş

Naskalı²,

Cemgil³

et al. 2022

Pervasive and Mobile Computing

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Section: Camera Pose Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An indoor localization dataset and data collection framework with high precision position annotation

Daniş

Naskalı²,

Cemgil³

et al. 2022

Pervasive and Mobile Computing

View full text Add to dashboard Cite

“…Starting from a known position, typically, the PDR technique recursively estimates the current location of an IMU-enabled device using two major processing tasks, i.e., heading estimation and step detection [17,18]. However, inaccurate heading estimation or false step detection will result in an inevitable cumulative error of the PDR based-positioning systems [19]. Furthermore, the initial coordinates and the heading of the user are strictly required for accurate PDR.…”

Section: Introductionmentioning

confidence: 99%

Online Dynamic Window (ODW) Assisted 2-Stage LSTM Indoor Localization for Smart Phones

Atashi

Mohammadi

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There has been a recent surge of interest on smart phone-based indoor localization due to the urgent need for real-time, accurate, and scalable indoor positioning solutions independent of any proprietary sensors/modules. Existing Inertial Measurement Unit (IMU)-based approaches, typically, use statistical and error prone heading and step length estimation techniques rendering them impractical for robust, real-time and accurate indoor positioning. In this regard, the paper takes one step forward to transfer offline IMU-based models to online positioning frameworks. More specifically, inspired by prominent advances in sequential Signal Processing (SP) and Natural Language Processing (NLP) techniques, two near real-time dynamic windowing mechanisms are proposed based on a two stage Long Short-Term Memory (LSTM) localization architecture. The two underlying LSTM architectures are trained with 2100 Action Units (AU). Compared to the traditional LSTM-based positioning approaches suffering from either high tensor computation requirements or low accuracy preventing them for real-time deployment, the proposed Online Dynamic Windowing (ODW) assisted two stage LSTM models can perform localization in a real-time fashion. Performance evaluations based on a real Pedestrian Dead Reckoning (PDR) dataset shows that the proposed model can achieve exceptional classification accuracy of 97.9% and 95.5% for the two underlying LSTMs.

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“…At present, there are some mature methods for the study of plane localization: the global navigation satellite system (GNSS) [10,11] and China's BeiDou navigation satellite system (BDS) [12], which play a major role in outdoor positioning; the pseudolite indoor positioning [13,14] and the indoor positioning system using the beacon [15,16] and Bluetooth [17]; in order to achieve seamless indoor and outdoor positioning services, Chinese researchers are developing a BDS/GPS indoor positioning pseudo-satellite system; precise positioning technology using real-time kinematics (RTK) [18]; using natural or artificial landmarks for positioning, such as two-dimensional code positioning [19]; signal guidance and positioning based on the sensor signals of visual camera [20], RFID [21], ultrasonic [22] and LiDAR [23]; positioning systems using Wi-Fi [24,25] such as (OS-ELM) [26]; inertial navigation and positioning (MEMS) [27]; odometer positioning [28]; etc.…”

Section: Introductionmentioning

confidence: 99%

An Orthogonal Wheel Odometer for Positioning in a Relative Coordinate System on a Floating Ground

Wang

et al. 2021

Applied Sciences

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This paper introduces a planar positioning sensing system based on orthogonal wheels and encoders for some surfaces that may float (such as ship decks). The positioning sensing system can obtain the desired position and angle information on any such ground that floats. In view of the current method of using the IMU gyroscope for positioning, the odometer data on these floating grounds are not consistent with the real-time data in the world coordinate system. The system takes advantage of the characteristic of the orthogonal wheel, using four vertical omnidirectional wheels and encoders to position on the floating ground. We design a new structure and obtain the position and angle information of a mobile robot by solving the encoder installed on four sets of omnidirectional wheels. Each orthogonal wheel is provided with a sliding mechanism. This is a good solution to the problem of irregular motion of the system facing the floating grounds. In the experiment, it is found that under the condition that the parameters of the four omnidirectional wheels are obtained by the encoder, the influence of the angle change of the robot in the world coordinate system caused by the flotation of the ground can be ignored, and the position and pose of the robot on the fluctuating ground can be well obtained. Regardless of straight or curved motion, the error can reach the centimeter level. In the mobile floating platform experiment, the maximum error of irregular movement process is 2.43 (±0.075) cm and the RMSE is 1.51 cm.

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An Accuracy Generalization Benchmark for Wireless Indoor Localization based on IMU Sensor Data

Cited by 8 publications

References 4 publications

An indoor localization dataset and data collection framework with high precision position annotation

An indoor localization dataset and data collection framework with high precision position annotation

Online Dynamic Window (ODW) Assisted 2-Stage LSTM Indoor Localization for Smart Phones

An Orthogonal Wheel Odometer for Positioning in a Relative Coordinate System on a Floating Ground

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