Visual-Inertial Odometry of Smartphone under Manhattan World

Wang, YuAn; Chen, Liang; Peng, Wei; Lu, Xiushan

doi:10.3390/rs12223818

Cited by 8 publications

(5 citation statements)

References 43 publications

(84 reference statements)

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“…In [6,22] and [23], researchers extended the MW into multiple MWs (Atlanta World) to better model the man-made environment, and estimated the directions of line features more accurately. However, the MW assumption in [7,24] and [25] is restrictive for scenes with curvy structures. For example, the horizontal directions of the environment are not orthogonal to each other, and the VSLAM system can only run after the MW has been initialized.…”

Section: Related Workmentioning

confidence: 99%

Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

Tang¹,

Wei²,

Cheng³

et al. 2022

Meas. Sci. Technol.

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This paper proposes an optimization-based stereo visual-inertial odometry (VIO) to locate indoor wheeled robots. Multiple Manhattan worlds assumption is adopted to model the interior environment. Instead of treating these worlds as isolated ones, we fuse the latest Manhattan world with the previous ones if they are in the same direction, reducing the calculated errors on the orientation of the latest Manhattan world. Then the structural lines which encode the orientation information of these worlds are taken as additional landmarks to improve positioning accuracy and reduce accumulated drift of the system, especially when the system is in a challenging environment (i.e., scenes with continuous turning and low textures). Besides, the structural lines are parameterized by only two variables, which improves the computational efficiency and simplifies the initialization of lines. Experiments on public benchmark datasets and in real-world environments demonstrate that the proposed VIO system can accurately position the wheeled robot in a complex indoor environment.

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Section: Related Workmentioning

confidence: 99%

Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

Tang¹,

Wei²,

Cheng³

et al. 2022

Meas. Sci. Technol.

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“…The abovementioned antenna's low performance is also a significant issue. However, it is critical to investigate the potential of smartphones' positioning abilities [40,41]. Certain VIO algorithms, such as VINS-mono, can estimate the temporal offset, and an appropriate integration can compen-sate for the sensors' shortcomings.…”

Section: Introductionmentioning

confidence: 99%

“…Peiliang Li modified VINS-mono and ported his VINS estimator to the iPhone 7 [51]. Yuan Wang and his team deployed VIO algorithms on an Android smartphone [40]. In recent years, some researchers applied machine learning and deep learning techniques to the subject of smartphone navigation.…”

Section: Introductionmentioning

confidence: 99%

The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

Niu

Guo

Shuai

et al. 2021

IJGI

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The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.

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“…Many studies consider using Signals of Opportunity (SOP) for positioning when GNSS is unavailable or unreliable. These SOP include digital television (Chen et al 2017b;Chen et al 2014), Bluetooth (Cao et al 2019), LEO (Chen, Wang, and Zhang 2016;Ardito et al 2019), Wi-Fi (Yan et al 2021(Yan et al , 2018(Yan et al , 2017, vision (Wang et al 2020;Chen et al 2017), and 5 G (Dammann, Raulefs, and Zhang 2015;Wymeersch et al 2017;Zhou et al 2020), and so on. Among them, the LEO satellite has been paid more and more attention and has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies consider using signals of opportunity (SOP) for positioning when GNSS is unavailable or unreliable. These signals of opportunity include digital television [9]- [11], Bluetooth [12], low earth orbit [13], [14], WIFI [15]- [17], vision [18], [19], and 5G [20]- [22], etc. Among them, the LEO satellite has been paid more and more attention and has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Chen

Shen

et al. 2021

Geo-spatial Information Science

Self Cite

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Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as an enhancement by means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is a prerequisite for providing enhanced LEO navigation services. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is crucial. In this work, the signal characteristics of LEO satellites: power space density in free space and the Doppler shift of LEO satellites are individually studied. The unified symbolic definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a Software Defined Receiver (SDR) is developed. The performance of these integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration. The performance of the coherent integration and differential integration algorithms is better than the other two integration algorithms, so the two algorithms are recommended for LEO signal acquisition and a 20 ms integration duration is preferred. The detection threshold of 2.5 is not suitable for all integration algorithms and various integration durations, especially for the Maximum-to-Mean Ratio indicator.

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Visual-Inertial Odometry of Smartphone under Manhattan World

Cited by 8 publications

References 43 publications

Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

Stereo visual-inertial odometry using structural lines for localizing indoor wheeled robots

The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

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