Sensor Integration and Image Georeferencing for Airborne 3D Mapping Applications By Sameh Nassar and Naser El-Sheimy University of Calgary, Canada
Contents Background INS/GPS Integration & Direct Georeferencing Improving Direct Georeferencing Accuracy MEMS for Accurate Georeferencing Is It Possible? Airborne Remote Sensing Required Accuracy 2 Conclusions & Recommendations
Background Airborne Remote Sensing Imaging Sensors: Frame-based (analog) aerial camera. CCD digital camera. Thermal camera. LiDAR. Pushbroom scanner. InSAR or SAR 3
Background Direct Georeferencing: Installing navigation sensors (for providing position & orientation), beside the imaging sensor, on board the aircraft to determine the EOPs directly. Overcomes indirect georeferencing limitations Navigation Sensor Candidates: 4 Global Positioning System (GPS) Inertial Navigation System (INS)
INS/GPS & Direct Georeferencing (1/2) r INS, v INS, att INS + r INS/GPS, v INS/GPS, att INS/GPS INS GPS r INS v INS + _ r v _ KF δr δv δatt r GPS, v GPS δr, δv, δatt, δaccel, δgyro 5
INS/GPS & Direct Georeferencing (2/2) b-frame (INS Frame) m R b r (t) m INS b a GPS GPS Antenna b a c m r GPS m r c m r j c r j (j) b R c c-frame (Imaging Sensor Frame) s r j c j Uncorrected Position m-frame (Mapping Frame) Georeferenced Position 6 r m j = r m GPS (t) + R m b (t).[s j. R b c. r c j + a b c a b GPS ]
Factors Affecting Direct Georeferencing Accuracy The complete INS and GPS processing chain (error control implementation and KF design) INS accuracy. GPS receiver quality. Alignment between the imaging & navigation sensors. Data collection circumstances and environment. 7 Depends on both the measurement & processing stages
Improving Direct Georeferencing Accuracy (1/3) Improve the quality of GPS data: - multiple reference GPS stations - minimum banking angles - short master-rover baseline - better ionospheric and tropospheric models - improved clocks - using GPS/GLONASS receivers 8
Improving Direct Georeferencing Accuracy (2/3) Using high quality inertial sensor technologies - very expensive (~ $ 150 K). Optimal system calibration & sensor placement: - INS and GPS constant errors - GPS and INS time synchronization - INS-imaging sensor relative orientation 9
Improving Direct Georeferencing Accuracy (3/3) Optimizing INS mathematical modeling (U of C): - improved error estimation & compensation better stochastic models of sensor errors (AR models of orders > 1). unscented Kalman filter (UKF). neural networks (NN). Allan Variance Analysis. near real-time bridging methods. 10 - data quality enhancement de-noising of inertial data (wavelet multi-resolution)
Better Stochastic Models of INS Sensor Errors (1/2) - Using Autoregressive (AR) models (orders > 1) 11 Static Data Static Data
Better Stochastic Models of INS Sensor Errors (2/2) - Using Autoregressive (AR) models (orders > 1) 12 Kinematic Data Kinematic Data
Unscented Kalman Filter (UKF) or Sigma Point KF 13 Kinematic Data Kinematic Data
Neural Networks 14 Kinematic Data Kinematic Data
Near Real-Time Bridging Methods Kinematic Data Kinematic Data 15
INS Data Quality Enhancement Data De-Noising (1/2) - Using Wavelet Multi-Resolution Analysis. 16 Kinematic Data Static Data
INS Data Quality Enhancement Data De-Noising (2/2) - Using Wavelet Multi-Resolution Analysis. 17 Coarse Alignment Fine Alignment
Airborne Remote Sensing Required Accuracy (1/2) Overall Accuracy (on the Ground) Depends on: INS/GPS navigation solution imaging sensor quality. image resolution. image scale. block geometry (in case of photogrammetry). type of ground coverage. terrain type. weather conditions (excluding InSAR). 18 However, the accuracy requirements have a very wide range and depend on the different applications.
Airborne Remote Sensing Required Accuracy (2/2) Mapping Application - Large Scale Engineering Projects - Cadastral Surveying Required Accuracy Position (m) Attitude (deg) 0.05 0.10 0.004 0.008 Cartographic Mapping (Scale 1:10,000) 1.00 5.00 0.167 0.334 Resource Mapping Forestry (Detailed) 0.20 1.00 0.017 0.050 Forestry (General) 2.00 5.00 0.334 0.500 Forest Fire Fighting (Hot Spot Detection) 1.00 5.00 0.167 0.500 Flood Hazard Mapping (Scale 1:6,000) 2.00 6.00 0.334 0.500 19 ** All numbers are expressed as RMS
Airborne Direct Georeferencing Obtained Accuracy Aerial Camera Imaging Sensor Obtained Accuracy Position (m) Attitude (deg) Scale > 1:2000 0.05 0.10 0.004 0.008 Scale < 1:5000 0.20 1.00 0.017 0.034 CCD Cameras 0.25 1.00 0.017 0.050 LiDAR 0.25 0.75 0.005 0.010 InSAR 0.50 5.00 0.050 0.500 Pushbroom Scanners 2.00 5.00 0.334 0.500 Thermal Cameras 1.00 3.00 0.167 0.500 20 ** All numbers are quite general and may vary based on the airborne application itself, flying height, pixel size, etc.
MEMS for Accurate Georeferencing Is It Possible? The MMSS group at U of C has developed its own MEMS IMU using 3 accelerometer & 3 gyro sensors from ADI. Properties: low cost (~ $25 per sensor) compact size (92x74x40 mm) low power (1/8 W) 21 Challenges: gyro drift < 1800 deg/h sensor noise >>>>>>
MEMS Navigation Accuracy (1/2) Kinematic Data Kinematic Data 22
MEMS Navigation Accuracy (2/2) Kinematic Data Kinematic Data 23
Inertial Systems for Direct Georeferencing State of the Art Performance Navigation- Grade IMU Tactical- Grade IMU Low-Cost Inertial Sensors Price (USD) 120-200K 25-100K 25-100$/axis Gyro Drift rate (deg/h) 0.005-0.010 0.1-10 100-6000 Accel. bias 50-100 mg 200-1000 mg 0.10-0.50 g Attitude Accuracy with DGPS 5-20 arcsec 0.3-10 arcmin 0.1-5 deg Sensors/ applications Film based aerial cameras & Land MM systems Digital aerial systems (including LiDAR & InSar) Portable MM (So far?) 24
Conclusions & Recommendations (1/2) Direct georeferencing has developed from a topic of academic interest to a commercially viable industry in various mapping applications. 3D georeferencing can be achieved with an accuracy sufficient for several mapping tasks using currently available imaging & navigation hardware. Development of mathematical modeling & advanced post-mission techniques will further increase the accuracy and robustness of the solutions. 25
Conclusions & Recommendations (2/2) 26 Considerable work is needed in the following areas: - real-time & post-mission quality control - automation of INS/GPS integration in case of frequent GPS signals loss of lock. - efficient manipulation & user-oriented of extremely large data bases. - improving the performance of low-cost (MEMS) inertial sensors. The result of solving these problems will be an enormous extension not only of digital 3D mapping but also for its fusion with other multi-sensor data.
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