Top

Earth Science Informatics

Published in:

28-02-2023 | METHODOLOGY

Precision mapping through the stereo vision and geometric transformations in unknown environments

Authors: Georgios Petrakis, Angelos Antonopoulos, Achilles Tripolitsiotis, Dimitris Trigkakis, Panagiotis Partsinevelos

Published in: Earth Science Informatics | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Geodetic techniques for surveying and rapid mapping need to be revisited due to the present progress on satellite, sensor, and geospatial technologies. Conventional surveying methods provide high level of accuracy but require significant human involvement in the field while GNSS (Global Navigation Satellite System) positioning method, provides unsatisfactory accuracy in urban or high vegetated areas due to the degraded GNSS signal coverage. In this study, an alternative surveying method is proposed which facilitates the process of characteristic point localization, using stereoscopic vision and at least one visual marker. At first, the camera system localizes itself and maps the study area using stereo SLAM (Simultaneous Localization and Mapping) algorithm while subsequently detects the visual markers (origin and targets), placed in the area. Afterwards, using a multi-view geometry method for the marker localization and an optimization algorithm for origin marker’s plane alignment, the system is able to export the coordinates of the markers and a point cloud (provided by SLAM) in a local coordinate system based on the origin marker’s pose and location. The study involves both terrestrial and unmanned aerial vehicle platforms that may carry the proposed equipment. An extensive set of indoor and outdoor, terrestrial and UAV experiments validates the methodology which succeeds a horizontal and vertical error in a level of 10 cm or better. To the best of our knowledge this study proposes the first surveying alternative which requires only a stereo camera and at least one visual marker in order to localize specific and arbitrary points in a centimeter level of accuracy. The proposed methodology, demonstrates that the use of low-cost equipment instead of the costly and complicated surveying equipment, may prove sufficient to produce an accurate 3D map of the scene in an unknown environment.

previous article Identification of The Survey Points from Network RTK Trajectory with Improved DBSCAN Clustering, Case Study on HNCORS

next article Determining the time response in GNSS tomographic modeling of troposphere

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Ampadu EG, Gebreslasie M, Ponce AM (2020) Mapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa. Remote Sens Appl: Soc Environ 18. https://doi.org/10.1016/j.rsase.2020.100302

Animesh S, Harsh S, Mangal K (2019) Autonomous detection and tracking of a high-speed ground vehicle using a quadrotor UAV. In: Proceedings of the AIAA Scitech Forum, San Diego, California, 7–11 January

Bañón L, Pagán JI, López I, Banon C, Aragonés L (2019) Validating UAS-based photogrammetry with traditional topographic methods for surveying dune ecosystems in the Spanish Mediterranean coast. J Mar Sci Eng 7:297. https://doi.org/10.3390/jmse7090297CrossRef

Bobbe M, Kern A, Khedar Y, Batzdorfer S, Bestmann U (2017) An automated rapid mapping solution based on ORB SLAM 2 and Agisoft Photoscan API. In: Proceedings of the IMAV, Toulouse, France

Burri M, Nikolic J, Gohl P, Schneider T, Rehder J, Omari S, Achtelik M, Siegwart R (2016) The euroc micro aerial vehicle datasets. Int J Robot Res. https://doi.org/10.1177/0278364915620033CrossRef

Carrera-Hernández JJ, Levresse G, Lacan P (2020) Is UAV-SfM surveying ready to replace traditional surveying techniques? Int J Remote Sens 41(12):4820–4837. https://doi.org/10.1080/01431161.2020.1727049CrossRef

Chiang KW, Tsai GJ, Chang HW, Joly C, EI-Sheimy N (2019) Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme. Inf Fusion 50:181–196. https://doi.org/10.1016/j.inffus.2019.01.004CrossRef

Chuang R (2018) Mapping surface breakages of the 2018 Hualien earthquake by using UAS photogrammetry. Terr Atmospheric Ocean Sci 30:351–366. https://doi.org/10.3319/TAO.2018.12.09.02CrossRef

Eldén L (1982) A weighted pseudoinverse, generalized singular values, and constrained least squares problems. BIT 22:487–502. https://doi.org/10.1007/BF01934412CrossRef

Gabrlik P (2015) The use of direct georeferencing in aerial photogrammetry with micro UAV. IFAC-PapersOnLine 48:380–385. https://doi.org/10.1016/j.ifacol.2015.07.064CrossRef

Gao X, Wang R, Demmel N, Cremers D (2018) LDSO: direct sparse odometry with loop closure. arXiv

Garrido-Jurado S, Muñoz SR, Madrid-Cuevas FJ, Medina-Carnicer R (2016) Generation of fiducial marker dictionaries using mixed integer linear programming. Pattern Recogn 51:481–491. https://doi.org/10.1016/j.patcog.2015.09.023CrossRef

Geiger A, Ziegler J, Stiller C (2011) Stereoscan: dense 3d reconstruction in real-time, in Intelligent Vehicles Symposium (IV), pp. 963–968. https://doi.org/10.1109/IVS.2011.5940405

Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on CVPR

Geneva P, Eckenhoff K, Lee W, Yang Y, Huang G (2020) Openvins: a research platform for visual-inertial estimation. In: 2020 ICRA, pp 4666–4672. https://doi.org/10.1109/ICRA40945.2020.9196524

Giubilato R, Chiodini S, Pertile M, Debei S (2018) An experimental comparison of ROS-compatible stereo visual SLAM methods for planetary rovers.https://doi.org/10.1109/MetroAeroSpace.2018.8453534

Haque A, Elsaharti A, Elderini T, Elsaharty MA, Neubert J (2020) UAV autonomous localization using macro-features matching with a CAD model. Sensors 20:743. https://doi.org/10.3390/s20030743CrossRef

Helgesen HH, Leira FS, Bryne TH, Albrektsen SM, Johansen TA (2019) Real-time georeferencing of thermal images using small fixed-wing UAVs in maritime environments. ISPRS J Photogramm Remote Sens 154:84–97. https://doi.org/10.1016/j.isprsjprs.2019.05.009CrossRef

Jende P, Nex F, Gerke M, Vosselman G (2018) A fully automatic approach to register mobile mapping and airborne imagery to support the correction of platform trajectories in GNSS-denied urban areas. ISPRS J Photogramm Remote Sens 141:86–99. https://doi.org/10.1016/j.isprsjprs.2018.04.017CrossRef

Kalacska M, Lucanus O, Arroyo-Mora JP, Laliberté É, Elmer K, Leblanc G, Groves A (2020) Accuracy of 3D landscape reconstruction without ground control points using different UAS platforms. Drones 4:13. https://doi.org/10.3390/drones4020013CrossRef

Kuroswiski AR, Oliveira NMF, Shiguemori EH (2018) Autonomous long-range navigation in GNSS-denied environment with low-cost UAV platform. In: Proceedings of the SysCon, Vancouver, Bc, Canada, 23 - 26 April. https://doi.org/10.1109/SYSCON.2018.8369592

Labb M, Michaud F (2013) Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Trans Robot 29(3):734–745. https://doi.org/10.1109/TRO.2013.2242375CrossRef

Lassila MM (2018) Mapping mineral resources in a living land: Sami mining resistance in Ohcejohka, northern Finland. Geoforum 96:1–9. https://doi.org/10.1016/j.geoforum.2018.07.004CrossRef

Li J, Besada JA, Bernardos AM, Tarrío P, Casar JR (2017) A novel system for object pose estimation using fused vision and inertial data. Inf Fusion 33:15–28. https://doi.org/10.1016/j.inffus.2016.04.006CrossRef

Ma Y, Li Q, Chu L, Zhou Y, Xu C (2021) Real-time detection and spatial localization of insulators for UAV inspection based on binocular stereo vision. Remote Sens 13:230. https://doi.org/10.3390/rs13020230CrossRef

McRae JN, Gay CJ, Nielsen BM, Hunt AP (2019) Using an unmanned aircraft system (drone) to conduct a complex high altitude search and rescue operation: a case study. Wilderness Environ Med 30:287–290. https://doi.org/10.1016/j.wem.2019.03.004CrossRef

Mishra B, Garg D, Narang P, Mishra V (2020) Drone-surveillance for search and rescue in natural disaster. Comput Commun 156:1–10. https://doi.org/10.1016/j.comcom.2020.03.012CrossRef

Munguía R, Urzua S, Bolea Y, Grau A (2016) Vision-based SLAM system for unmanned aerial vehicles. Sensors 16:372. https://doi.org/10.3390/s16030372CrossRef

Mur-Artal R, Tardós JD (2017) ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Trans Robot 33:1255–1262. https://doi.org/10.1109/TRO.2017.2705103CrossRef

Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRef

Panigrahi N, Doddamani SR, Singh M, Kandulna BN (2015) A method to compute location in GNSS denied area. IEEE International CONECCT 1–5. https://doi.org/10.1109/CONECCT.2015.7383907

Partsinevelos P, Chatziparaschis D, Trigkakis D, Tripolitsiotis AA (2020) Novel UAV-assisted positioning system for GNSS-denied environments. Remote Sens 12:1080. https://doi.org/10.3390/rs12071080CrossRef

Pinto AM, Matos AC (2020) MARESye: a hybrid imaging system for underwater robotic applications. Inf Fusion 55:16–29. https://doi.org/10.1016/j.inffus.2019.07.014CrossRef

Pire T, Fischer T, Civera J, Cristforis P, Berlles J (2015) Stereo parallel tracking and mapping for robot localization in Proc. IROS, pp 1373–1378. https://doi.org/10.1109/IROS.2015.7353546

Qin T, Cao S, Pan J, Shen S (2019) A general optimization-based framework for global pose estimation with multiple sensors, arXiv

Romero-Ramirez FJ, Muñoz-Salinas R, Medina-Carnicer R (2018) Speeded up detection of squared fiducial markers. Image Vis Comput 76:38–47. https://doi.org/10.1016/j.imavis.2018.05.004CrossRef

Rosinol A, Abate M, Chang Y, Carlone L (2020) Kimera: an open-source library for real-time metric-semantic localization and mapping. In: IEEE Intl. Conf. on Robotics and Automation (ICRA)

Rublee E, Rabaud V, Konolige K. and Bradski G, (2011) ORB: an efficient alternative to SIFT or SURF. International conference on computer vision, pp 2564–2571. https://doi.org/10.1109/ICCV.2011.6126544

Sahoo B, Biglarbegian M, Melek W (2021) Monocular visual inertial direct SLAM with robust scale estimation for ground robots/vehicles. Robotics 10:23. https://doi.org/10.3390/robotics10010023CrossRef

Samuel B (2004) Introduction to inverse kinematics with Jacobian transpose, pseudoinverse and damped least squares methods. IEEE Trans Robot Autom 17:16

Schneider T, Dymczyk M, Fehr M, Egger K, Lynen S, Gilitschenski I, Siegwart R (2018) Maplab: an open framework for research in visual-inertial mapping and localization. IEEE Robot Autom Lett. https://doi.org/10.1109/LRA.2018.2800113CrossRef

Sharafutdinov D, Griguletskii M, Kopanev P, Kurenkov M, Ferrer G, Burkov A, Gonnochenko A, Tsetserukou D (2021) Comparison of modern open-source visual SLAM approaches, arXiv

Shi X, Li D, Zhao P, Tian Q, Tian Y, Long Q, Zhu C, Song J, Qiao F, Song L, Guo Y, Wang Z, Zhang Y, Qin B, Yang W, Wang F, Chan R, She Q (2020) Are we ready for service robots? The OpenLORIS-Scene datasets for lifelong SLAM. ICRA 2020, pp 3139–3145

Sumikura S, Shibuya M, Sakurada K (2019) Open-VSLAM: a versatile visual SLAM framework. In: Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, New York, NY, USA, pp 2292–2295. https://doi.org/10.1145/3343031.3350539

Tang J, Chen Y, Niu X, Wang L, Chen L, Liu J, Shi C, Hyyppä J (2015) LiDAR scan matching aided inertial navigation system in GNSS-denied environments. Sensors 15:16710–16728. https://doi.org/10.3390/s150716710CrossRef

Tomaštík J, Mokroš M, Saloň Š, Chudý F, Tunák D (2017) Accuracy of photogrammetric UAV-based point clouds under conditions of partially-open forest canopy. Forests 8:151. https://doi.org/10.3390/f8050151CrossRef

Trigkakis D, Petrakis G, Tripolitsiotis A, Partsinevelos P (2020) Automated geolocation in urban environments using a simple camera-equipped unmanned aerial vehicle: a rapid mapping surveying alternative? ISPRS Int J Geo-Inf 9:425. https://doi.org/10.3390/ijgi9070425CrossRef

Urzua S, Munguia R, Grau A (2017) Vision-based SLAM system for MAVs in GPS-denied environments. Int J Micro Air Veh 9:283–296. https://doi.org/10.1177/1756829317705325CrossRef

Usenko V, Demmel N, Schubert D, Stückler J, Cremers D (2019) Visual-inertial mapping with non-linear factor recovery. arXiv. https://doi.org/10.1109/LRA.2019.2961227

Vrba M, Saska M (2020) Marker-less micro aerial vehicle detection and localization using convolutional neural networks. IEEE Robot Autom Lett 5:2459–2466. https://doi.org/10.1109/LRA.2020.2972819CrossRef

Vrba M, Heřt D, Saska M (2019) Onboard marker-less detection and localization of non-cooperating drones for their safe interception by an autonomous aerial system. IEEE Robot Autom Lett 4:3402–3409. https://doi.org/10.1109/LRA.2019.2927130CrossRef

Xu L, Feng C, Kamat VR, Menassa CC (2019) An Occupancy Grid Mapping enhanced visual SLAM for real-time locating applications in indoor GPS denied environments. Autom Constr 104:230–245. https://doi.org/10.1016/j.autcon.2019.04.011CrossRef

Yang T, Ren Q, Zhang F, Xie B, Ren H, Li J, Zhang Y (2018) Hybrid camera array-based UAV auto-landing on moving UGV in GPS-denied environment. Remote Sens 10:1829. https://doi.org/10.3390/rs10111829CrossRef

Zahran S, Moussa A, El-Sheimy N (2018) Enhanced UAV navigation in GNSS denied environment using repeated dynamics pattern recognition. IEEE/ION PLANS, 1135–1142. https://doi.org/10.1109/PLANS.2018.8373497

Zhang C, He T, Zhan Q, Hu X (2019) Visual navigation based on stereo camera for water conservancy UAVs. In: Proceedings of the ICIST, Hulunbuir, China.https://doi.org/10.1109/ICIST.2019.8836851

Title: Precision mapping through the stereo vision and geometric transformations in unknown environments
Authors: Georgios Petrakis
Angelos Antonopoulos
Achilles Tripolitsiotis
Dimitris Trigkakis
Panagiotis Partsinevelos
Publication date: 28-02-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 2/2023
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-00972-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2023

Groundwater Quality Analysis and Drinkability Prediction using Artificial Intelligence

Inversion model for snow geophysical parameters estimation using sentinel–1 stokes parameter

Correction to: Automatic lithology identification method based on efficient deep convolutional network

Exploring watershed structural variation during watershed evolution process under artificial rainfall experiment

Machine learning for earthquake prediction: a review (2017–2021)

Research on stability analysis of large karst cave structure based on multi-source point clouds modeling

Premium Partner