RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
<p>Visualization of frame <math display="inline"><semantics> <mi>A</mi> </semantics></math> and frame <math display="inline"><semantics> <mi>B</mi> </semantics></math>. Frame <math display="inline"><semantics> <mi>A</mi> </semantics></math> rotates an angle <math display="inline"><semantics> <mi>θ</mi> </semantics></math> around the <math display="inline"><semantics> <mi>r</mi> </semantics></math> axis to become frame <math display="inline"><semantics> <mi>B</mi> </semantics></math>.</p> "> Figure 2
<p>Visualization of the various GNSS carrier frequencies.</p> "> Figure 3
<p>Close-up of tying the Xiaomi Mi 8 to our foot.</p> "> Figure 4
<p>RTK performances in static mode. (<b>a</b>) The visible signals; (<b>b</b>) vertical positioning errors; (<b>c</b>) horizontal positioning errors (single frequency); (<b>d</b>) horizontal positioning errors (dual frequency).</p> "> Figure 5
<p>View of the base station and the rover. (<b>a</b>) View of the M300 receiver; (<b>b</b>) view of the antenna on the roof; (<b>c</b>) view of the rover; (<b>d</b>) brief description of the rover.</p> "> Figure 6
<p>RTK performances based on the NovAtel receiver. (<b>a</b>) Visible signals and satellites; (<b>b</b>) number of different signals); (<b>c</b>) single-frequency RTK; (<b>d</b>) dual-frequency RTK.</p> "> Figure 7
<p>Signals tracked by the Xiaomi Mi 8. (<b>a</b>) Visible signals and satellites; (<b>b</b>) number of signals.</p> "> Figure 8
<p>RTK performances on the sports ground. (<b>a</b>) GPS(L1); (<b>b</b>) GPS(L1 + L5); (<b>c</b>) GPS(L1) + Galileo(E1); (<b>d</b>) GPS(L1 + L5) + Galileo(E1 + E5a).</p> "> Figure 9
<p>RTK performances shown in Google Earth.</p> "> Figure 10
<p>RTK performances on the basketball court. (<b>a</b>) GPS(L1) + Galileo(E1); (<b>b</b>) GPS(L1 + L5) + Galileo(E1 + E5a).</p> "> Figure 11
<p>The coordinate system used by the Android system.</p> "> Figure 12
<p>The performance of the Madgwick algorithm.</p> "> Figure 13
<p>The performance of the ZUPT aiding INS. (<b>a</b>) Place where we walked; (<b>b</b>) the estimated trajectory.</p> "> Figure 14
<p>A narrow path with tall teaching buildings on both sides.</p> "> Figure 15
<p>Comparison between the performances of RTK without the assistance of an IMU-based pedestrian navigation algorithm and RTK with the assistance of an IMU-based pedestrian navigation algorithm. (<b>a</b>) The performance of RTK without the assistance of an IMU-based pedestrian navigation algorithm; (<b>b</b>) the performance of RTK with the assistance of an IMU-based pedestrian navigation algorithm.</p> "> Figure 16
<p>Trajectories in Google Earth.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Implementation of RTK
2.1.1. EKF Formulation
2.1.2. Theory of RTK
2.2. Introduction of the Madgwick Algorithm
2.2.1. Introduction of the Quaternion
2.2.2. Theory of the Madgwick Algorithm
2.3. Introduction of ZUPT Aiding INS
2.3.1. Zero-Velocity Detection
2.3.2. Updating the State with Virtual Measurements
2.4. RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm
2.5. Data Collection
2.5.1. GNSS Raw Measurements’ Collection
2.5.2. IMU Raw Measurements Collection
3. Results
3.1. RTK Performances Based on the Xiaomi Mi 8
3.1.1. RTK Performances in Static Mode
3.1.2. RTK Performances in Dynamic Mode
3.2. The Performance of the Madgwick Algorithm
3.3. The Performance of the ZUPT Aiding INS
3.4. Performances of RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm
4. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Modes | GPS(L1) | GPS(L1/L5) | GPS(L1) + Gal(E1) | GPS(L1/L5) + Gal(E1/E5a) | |
---|---|---|---|---|---|
X | 2.4395 | 1.3852 | 0.3424 | 0.0111 | |
Mean (m) | Y | −5.2601 | −1.5211 | −1.5423 | 0.2136 |
Z | −2.2977 | −1.1398 | −0.0815 | 0.1150 | |
Norm | 6.2370 | 2.3520 | 1.5819 | 0.2428 | |
X | 7.8644 | 3.3493 | 1.2287 | 0.5905 | |
RMS (m) | Y | 5.8325 | 3.7130 | 3.0119 | 0.9283 |
Z | 5.3058 | 1.9419 | 0.8935 | 0.4540 | |
Norm | 11.1364 | 5.3643 | 3.3734 | 1.1902 |
Modes | GPS(L1) + Gal(E1) | GPS(L1/L5) + Gal(E1/E5a) | |
---|---|---|---|
X | 0.2175 | 0.2002 | |
Mean (m) | Y | −1.6638 | −1.1142 |
Z | −0.4090 | −0.5422 | |
Norm | 1.7271 | 1.2552 | |
X | 0.7182 | 0.3727 | |
RMS (m) | Y | 1.8605 | 1.4237 |
Z | 1.9676 | 1.2460 | |
Norm | 2.8015 | 1.9284 |
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Niu, Z.; Nie, P.; Tao, L.; Sun, J.; Zhu, B. RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones. Sensors 2019, 19, 3228. https://doi.org/10.3390/s19143228
Niu Z, Nie P, Tao L, Sun J, Zhu B. RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones. Sensors. 2019; 19(14):3228. https://doi.org/10.3390/s19143228
Chicago/Turabian StyleNiu, Zun, Ping Nie, Lin Tao, Junren Sun, and Bocheng Zhu. 2019. "RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones" Sensors 19, no. 14: 3228. https://doi.org/10.3390/s19143228