GPS-LiDAR Sensor Fusion Aided by 3D City Models for UAVs Akshay Shetty and Grace Xingxin Gao

Transcription

1 GPS-LiDAR Sensor Fusion Aided by 3D City Models for UAVs Akshay Shetty and Grace Xingxin Gao SCPNT, November 2017

2 Positioning in Urban Areas GPS signals blocked or reflected Additional sensors: LiDAR, cameras, etc 1 1

3 LiDAR State Estimation Challenge Surrounding features affect accuracy Need to characterize covariance accordingly Start End [ [Google Earth] 2 2

4 State Estimation Covariance Adequate features Poor features Lack of features [Google Earth] [Google Earth] [Google Earth] 3 3

5 Approach Deep sensor fusion Characterize LiDAR-based position covariance based on features Eliminate NLOS satellites Use 3D city model to detect and eliminate NLOS GPS satellites 4 4

6 Outline Approach: Overall Architecture 3D City Model LiDAR-based State Estimation and Covariance GPS Measurement Model Experimental Setup and Results Summary Future Work: Deep Learning 5 5

7 Overall Architecture 6 6

8 3D City Model Illinois Geospatial Data Clearinghouse provides top-view point cloud [ OpenStreetMap provides building footprint information [ Top-view point cloud from geospatial data Building wall information from OpenStreetMap 7 7

9 LiDAR Odometry Use Iterative Closest Point (ICP) algorithm Match consecutive point clouds to estimate incremental motion ICP Reference Point Cloud Input Point Cloud 8 8

10 LiDAR 3D City Model Use ICP algorithm Match LiDAR point cloud with 3D city model ICP Before Matching After Matching 9 9

11 LiDAR 3D City Model True Position Initial Positions Final Positions Feature distribution Position accuracy [Google Earth] [Google Earth] Adequate High Poor Low [Google Earth] [Google Earth] 10 10

12 LiDAR Point Cloud Features LiDAR-based position covariance as function of features Extract feature points based on curvature values [Zhang et al., 2014] LiDAR Point Cloud Surface Points Edge Points 11 11

13 Surface Feature Points Covariance Ellipsoid Surface Normal Orthonormal Basis 12 12

14 Edge Feature Points Covariance Ellipsoid Edge Direction Orthonormal Basis 13 13

15 Combined Position Covariance LiDAR-based position covariance: Covariance Ellipsoid LiDAR Point Cloud Surface Points Edge Points 14 14

16 GPS Measurement Model Pseudorange measurement: Speed of light Clock biases Atmospheric errors Measurement noise Double-difference measurement: Measurement covariance: 15 15

17 Non-line-of-sight (NLOS) Satellites Eliminate satellites blocked by 3D city model 16 16

18 Outline Approach: Overall Architecture 3D City Model LiDAR-based State Estimation and Covariance GPS Measurement Model Experimental Setup and Results Summary Future Work: Deep Learning 17 17

19 Experimental Setup Custom-built ibqr UAV LiDAR IMU GPS Antenna GPS Receiver Onboard Computer 18 18

20 Results: Individual Measurements GPS unweighted least squares estimate contains large errors LiDAR odometry drifts over time, due to poor distribution of features in some sections LiDAR 3D city model matching contains errors where ICP might converge to local minima 19 19

21 Results: Sensor Fusion Our covariance model v/s fixed covariance model Our algorithm matches true path more accurately compared to a fixed covariance model 20 20

22 Outline Approach: Overall Architecture 3D City Model LiDAR-based State Estimation and Covariance GPS Measurement Model Experimental Setup and Results Summary Future Work: Deep Learning 21 21

23 Summary Proposed a deep sensor fusion architecture for GPS and LiDAR Implemented a novel method to characterize LiDAR-based position covariance Applied a 3D city model to eliminate NLOS satellites Validated improvement in positioning accuracy using proposed technique 22 22

24 Outline Approach: Overall Architecture 3D City Model LiDAR-based State Estimation and Covariance GPS Measurement Model Experimental Setup and Results Summary Future Work: Deep Learning 23 23

25 Deep Learning for Sensor Fusion Develop deep learning for different components 24 24

26 Deep Learning Dataset for GPS Experimental vehicle with 10 GPS receivers Collected data near San Francisco: downtown, underground, open areas, etc. Intermediate measurements such as pseudoranges, carrier phases, SNR High-grade IMU for ground truth 25 25

27 Deep Learning Dataset for LiDAR Simulations in Unity Game Engine 26 26

28 Thank you! Acknowledgement We would like to thank Kalmanje Krishnakumar and his group at NASA Ames for supporting this work under the grant NNX17AC13G 27 27