Assessing 3D Point Cloud Fidelity of UAS SfM Software Solutions Over Varying Terrain

Transcription

1 Assessing 3D Point Cloud Fidelity of UAS SfM Software Solutions Over Varying Terrain Michael Schwind, Michael J. Starek (Presenter) 18th Annual JALBTCX Airborne Coastal Mapping and Charting Technical Workshop, June 2017

2 Introduction Pix4D and Agisoft PhotoScan are well known commercial SfM photogrammetry software for UAS (drone) image processing. User-friendly, powerful, flexible, and offer good documentation and customer support. But can be relatively expensive. OpenDroneMap project is a suite of open source tools for UAS-SfM processing. Open source software removes expense, but the user should be prepared to have less software support and less control over the processing.

3 Structure from Motion (SfM) Workflow Simultaneous camera pose & scene geometry Match features in multiple overlapping images Feature detection e.g. SIFT Feature correspondence Sparse 3D Reconstruction Dense 3D matching Pose Reconstruction point cloud, DSM, ortho. SfM software offer many parameters but are black box in many aspects. Performance can vary dependent on many factors including terrain.

4 Objective Compare resulting 3D point clouds from Pix4D, Agisoft PhotoScan, and OpenDroneMap generated from a sequence of images acquired using a small UAS equipped with a consumer-grade digital camera: qualitative, statistical, cloud-to-cloud distance (Cloud Compare), absolute accuracy Goal: assess performance by evaluating results over varying terrain/texture We are not promoting one software or another! Simply reporting results

5 Marsh Study Areas Beach & Jetties Parking Lot Forested Peninsula House control no control

6 Methodology Data Collection DJI Phantom 3 Professional Sony EXMOR 12.4 M camera 2.82 pounds 23 min flight time Pix4D Capture Application Number of Images Percent Overlap Camera Angle (%) Altitude (m) Image Resolution (cm) Area Covered (m²) Marsh ,002 Beach and Jetties ,956 Parking Lot ,843 House ,630 Forested Peninsula ,888

7 Methodology Grount Truth z Comparison Ground control targets were used during UAS parking lot survey Riegl VZ-400 Terrestrial Laser Scan 600 m range 3 mm precision, 1σσ at 100m 20 millidegree scan angle ~1 cm pt spacing at 50 m filtered point cloud to remove non-ground objs.

8 Agisoft PhotoScan Parameters Methodology Data Processing High accuracy makes software work with the original photo size; enables more accurate pose Sparse Point Cloud In the reference mode, the images are selected based on the measured camera locations. Speeds up feature matching Upper limit of key point features per image Dense Point Cloud Upper limit of tie point features used per photo (default) ultra-high quality means densifying at the original photo size/gsd. Depth filtering is used to filter out outlying points caused by noisy or badly focused imagery. Mild

9 Pix4D Parameters Methodology Data Processing Allows the user to specify the number of keypoints to be extracted. Defines the scale of the images at which additional 3D points are computed. Original image size was used This parameter defines the density of the point cloud. Set to high, creates a point per image scale pixel. Tried to use similar settings to Photoscan where possible to make fair assessment.

10 Methodology Data Processing odm-vagrant OpenDroneMap links multiple open source software together. These software are OpenDroneMap, CMVS, and PMVS. Each run time parameter must specify which link of the open source chain it controls. Limited control: e.g. min_num of features and density; defaults used

11 Results

12 Results Things to point out: Bowl effect observed in Pix4D solution Vertical separation between point clouds PhotoScan Pix4D Likely from insufficient self-calibration (mainly radial distortion?), results in deformation 30.05% difference between the initial and optimized internal camera parameters for Pix4D

13 Results

14 Results

15 Likely cause of peninsula reconstruction failure Calm and reflective water features tricked the automated feature extraction and correspondence during SfM processing with PhotoScan/ODM (Pix4D handled it) masking the water can fix it, increasing tie points

16 Results

17 Tilt observed between UAS and TLS; VRS RTK GPS error in a control target (accuracy likely better) Results Cloud-to-Cloud Distances Noisier SfM Smoother Lidar

18 Point Cloud Characteristics Marsh Beach/Jetties Forested Peninsula Parking Lot *Ground Control House Results Key Point Matches Density (ppm²) Spacing (m) No. of Points 23,728 3, ,954,551 Agisoft Photoscan 644,740 3, ,394,900 Pix4D 323, ,935,784 OpenDroneMap 33,955 2, ,840,450 Agisoft Photoscan 427,372 3, ,769,094 Pix4D 207, ,132,233 OpenDroneMap 11,672 1, ,378,076 Agisoft Photoscan 114,291 2, ,996,628 Pix4D 39, ,861 OpenDroneMap 35,397 7, ,960,767 Agisoft Photoscan 953,894 9, ,510,589 Pix4D 436, ,010,131 OpenDroneMap 109, ,879,269 Agisoft Photoscan 1,842, ,638,333 Pix4D 427, ,111 OpenDroneMap Point Cloud Z Statistics Marsh Beach/Jetties Forested Peninsula Parking Lot *Ground Control House Min Z (m) Max Z (m) Mean Z (m) StdDev Z (m) Agisoft Photoscan Pix4D OpenDroneMap Agisoft Photoscan Pix4D OpenDroneMap Agisoft Photoscan Pix4D OpenDroneMap Agisoft Photoscan Pix4D OpenDroneMap Agisoft Photoscan Pix4D OpenDroneMap

19 Vertical Accuracy Pix4D Value (m) Class Pixel Count Area m² Percentage % cm > dz > - 4 cm Agisoft PhotoScan Value (m) Class Pixel Count Area m² Percentage % cm > dz > - 4 cm OpenDroneMap Value (m) Class Pixel Count Area m² Percentage % cm > dz > - 4 cm

20 Take Away uncertainty in UAS-SfM software driven by many factors including terrain Conclusion + point cloud (PC) with the highest density 4 out of 5 surveys + successful at reconstructing every terrain type % of z-values fell within ~4 cm of the LiDAR - without control, bowl effect observed even with geotagging + best at 3D reconstruction of house + no bowl effect observed without control % of z-values fell within ~4 cm of the LiDAR - less dense PC, failed reconstruction of peninsula and some veg. + Free! Ongoing project with people contributing (improves) + dense enough PC for many DEM applications, decent orthos - lowest fidelity reconstruction, importing control is not precise Although we made efforts to make this a fair assessment, the range of parameter settings between software means these results should be taken with caution.

21 UAS Lidar Riegl VUX-LR Pulse Aerospace Vapor 55 Up to 1350 m 60 reflectivity SPAN INS with Litef µimu 200 Hz

22 Example page 022

23 Example page 023

24 Thank You JALBTCX! Questions? Contact Michael J. Starek Geospatial Surveying Engineering and GISc Texas A&M University-Corpus Christi,

25 Mustang Island, March 2017 UAS-SfM Terrestrial Lidar ($1k drone, $400 camera) ($100+k sensor)