Lever-arm and boresight correction, and field of view determination of a spectroradiometer mounted on an unmanned aircraft system

Abstract This study addresses the correction of lever-arm offset and boresight angle, and field of view (FOV) determination to enable accurate footprint determination of a spectroradiometer mounted on an unmanned aircraft system (UAS). To characterise the footprint, an accurate determination of the spectroradiometer position and orientation (pose) must be acquired with a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). Accurate pose estimation requires an accurate lever-arm and boresight correction between the pose measuring sensors and the spectroradiometer. Similarly, the spectroradiometer FOV is required to determine the footprint size as a function of above ground level (AGL) flying height. The system used in this study consists of an IMU with dual-frequency and dual-antenna GNSS receiver, a machine vision camera, and a point-measuring spectroradiometer (Ocean Optics QE Pro). The lever-arm offset was determined from a scaled 3D point cloud of the system, created using photos of the airframe and processed with the structure-from-motion (SfM) algorithm. The boresight angles were estimated with stationary experiments by computing the difference between the orientations of the IMU, the spectroradiometer, and the camera. The orientation of the spectroradiometer was determined by moving a spectrally distinct target into the FOV. The orientation of IMU was measured by averaging its readings during the stationary epoch, while SfM was employed as an independent technique to estimate the orientation of the camera. The footprint of the spectroradiometer for a combination of AGL height and Gershun tube aperture ring was determined experimentally, enabling computation of the effective FOV. In-flight validation of the lever-arm and boresight correction was performed by comparing the corrected pose of the co-mounted camera with the pose derived from SfM as the reference. Our experimental results demonstrate that controlled determination and correction of lever-arm and boresight increases the pose estimation accuracy and thereby supports the direct georeferencing of a UAS-mounted spectroradiometer point observation.

[1]  Eija Honkavaara,et al.  Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..

[2]  Eija Honkavaara,et al.  Upscaling of solar induced chlorophyll fluorescence from leaf to canopy using the DART model and a realistic 3D forest scene , 2017 .

[3]  Derek D. Lichti,et al.  Rigorous approach to bore-sight self-calibration in airborne laser scanning , 2006 .

[4]  Karsten Jacobsen,et al.  Influence of System Calibration on Direct Sensor Orientation , 2005 .

[5]  Jan Skaloud,et al.  A micro-UAV with the capability of direct georeferencing , 2013 .

[6]  M. Schaepman,et al.  Far-red sun-induced chlorophyll fluorescence shows ecosystem-specific relationships to gross primary production: An assessment based on observational and modeling approaches , 2015 .

[7]  Arko Lucieer,et al.  An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..

[8]  Roland Siegwart,et al.  Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV , 2012, 2012 IEEE International Conference on Robotics and Automation.

[9]  Kai-Wei Chiang,et al.  The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications , 2012, Sensors.

[10]  R. Colombo,et al.  Red and far red Sun‐induced chlorophyll fluorescence as a measure of plant photosynthesis , 2015 .

[11]  M. Rossini,et al.  Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity , 2016 .

[12]  Arnon Karnieli,et al.  Exploring field-of-view non-uniformities produced by a hand-held spectroradiometer , 2011 .

[13]  R. W. Leamer,et al.  Calibration of a Field Spectroradiometer , 1973 .

[14]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[15]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[16]  Luis Alonso,et al.  Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications , 2009 .

[17]  Matthew F. McCabe,et al.  PUSHBROOM HYPERSPECTRAL IMAGING FROM AN UNMANNED AIRCRAFT SYSTEM (UAS) – GEOMETRIC PROCESSINGWORKFLOW AND ACCURACY ASSESSMENT , 2017 .

[18]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[19]  Martin Pfennigbauer,et al.  Boresight alignment method for mobile laser scanning systems , 2010 .

[20]  Ayman F. Habib,et al.  Alternative Methodologies for LiDAR System Calibration , 2010, Remote. Sens..

[21]  P. Zarco-Tejada,et al.  Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture , 2016 .

[22]  Sharon A. Robinson,et al.  Do Daily and Seasonal Trends in Leaf Solar Induced Fluorescence Reflect Changes in Photosynthesis, Growth or Light Exposure? , 2017, Remote. Sens..

[23]  C. Glennie.  Rigorous 3D error analysis of kinematic scanning LIDAR systems , 2007 .

[24]  Naser El-Sheimy,et al.  A New Calibration Method Using Low Cost MEM IMUs to Verify the Performance of UAV-Borne MMS Payloads , 2015, Sensors.

[25]  Paula Debiasi,et al.  A Study on In Situ Calibration of an Off-The-Shelf Digital Camera Integrated to a Lidar System , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Arko Lucieer,et al.  HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System , 2014, J. Field Robotics.

[27]  Matthew O. Anderson,et al.  Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle , 2012, Remote. Sens..

[28]  Stefano Amaducci,et al.  Fluorescence, PRI and canopy temperature for water stress detection in cereal crops , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[29]  L. Guanter,et al.  Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model , 2019, Remote Sensing of Environment.

[30]  Costas Armenakis,et al.  Use of UAV-Borne Spectrometer for Land Cover Classification , 2018 .

[31]  Yi Lin,et al.  Mini-UAV-Borne LIDAR for Fine-Scale Mapping , 2011, IEEE Geoscience and Remote Sensing Letters.

[32]  Juha Suomalainen,et al.  A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles , 2014, Remote. Sens..

[33]  A. Burkart,et al.  A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy , 2014, IEEE Sensors Journal.

[34]  Uwe Rascher,et al.  Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress , 2015 .

[35]  Sergio Cogliati,et al.  Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS , 2017, Remote. Sens..

[36]  Craig L. Glennie,et al.  Synthesis of Transportation Applications of Mobile LIDAR , 2013, Remote. Sens..

[37]  Arko Lucieer,et al.  Comparison of MEMS-based and FOG-based IMUs to determine sensor pose on an unmanned aircraft system , 2017 .

[38]  Arko Lucieer,et al.  Error Budget for Geolocation of Spectroradiometer Point Observations from an Unmanned Aircraft System , 2018, Sensors.

[39]  C. Thom,et al.  Study of lever-arm effect using embedded photogrammetry and on-board gps receiver on uav for metrological mapping purpose and proposal of a free ground measurements calibration procedure, , 2016 .

[40]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[41]  R. Colombo,et al.  Sun‐induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant , 2015, Global change biology.

[42]  Timothy J. Malthus,et al.  The Fields of View and Directional Response Functions of Two Field Spectroradiometers , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[43]  M. Rossini,et al.  Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems , 2015 .

[44]  Stergios I. Roumeliotis,et al.  A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation , 2008, IEEE Transactions on Robotics.

[45]  Gonzalo Pajares,et al.  Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .