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18th Place Solution in GSDC

5473f177bf5562e68645e1a6e0428f9c?s=47 Kyohei Uto
August 06, 2021

18th Place Solution in GSDC

Google Smartphone Decimeter Challengeで18位になりました。


Kyohei Uto

August 06, 2021


  1. Google Smartphone Decimeter Challenge Pre/Post processing (PP) Team: JTC Private

    LB: 18th (2.841) Reproducing baseline Main process OSR data from swift-nav Calculation for satellite position (GPS only) derived.csv reproduced baseline location Reproduce baseline location by Weighted Least Squares(WLS) used satellite position and corrected pseudorange ・Weight of WLS are hand-tuned. ・Use only GPS/GALILEO/QZS ・Use scalculated satellite position(GPS only) given baseline location (CV:5.29) reproduced baseline location v1 (CV:5.46) ① Blending baseline location GnssLog.txt PP 1. Outlier Correction 2. Kalman Smoothing 3. Phone Mean 4. Remove Phone 5. Snap to Grid 6. Phone Mean 7. Stop Mean lightGBM PP 1. Kalman Smoothing 2. Snap to Grid 3. Stop Mean 4. Phone Mean IMU data(acce, gyro, magn, rot) Apply only downtown area reproduced baseline location v2 (CV:5.21) ×0.5 ×0.25 ×0.25 ③ Estimation for location by IMU data ② Pre Processing ④ Post Processing Weighted average Area classification Copyright 2021 @kuto_bopro Apply Kalman Smoothing to IMU data and create lag/agg features blend location (CV:4.95) Outlier Correction ・Judge outlier based on 2σ and movement speed > 45m/s ・Apply linear interpolation to outlier by previous/next position Remove Phone ・Remove data from a specific smartphone and interpolate with data from other smartphones. Phone Mean ・ ・Mean at the smart phone locations in the same collection and epoch (epochs were interpolated) Snap to Grid ・snap prediction points to ground truth grids ・apply downtown and difficult area Stop Mean ・predict car speed by lightGBM ・target: speedMps in ground truth ・apply mean for stop groups Kalman Smoothing ・States:Lat/Lon and epoch ・Linear interpolation to keep epoch width constant lightGBM car speed grouping if speed < 0.95m/s for 2s gr1 gr2 gr3 mean mean mean lag/agg feats of location openstreetmap collections more 5m error points highway (open sky) normal downtown (high multi path) difficult area (bridge etc...) If the most common attribute in the "highway" column is motorway if mean degree >= 3 in road graph network other make buffer (point to polygon) ground truth grids prediction points