Integrity of Visual Navigation—Developments, Challenges, and Prospects | NAVIGATION: Journal of the Institute of Navigation

REFERENCES

↵
1. Al Hage, J.,
2. Xu, P., &
3. Bonnifait, P.
(2019). High integrity localization with multi-lane camera measurements. 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France. https://doi.org/10.1109/ivs.2019.8813988
↵
1. Bay, H.,
2. Tuytelaars, T., &
3. Van Gool, L.
(2006). SURF: Speeded up robust features. Computer Vision – ECCV 2006, Graz, Austria, 404–417. https://doi.org/10.1007/11744023_32
↵
1. Bhamidipati, S., &
2. Gao, G. X.
(2019). SLAM-based integrity monitoring using GPS and fish-eye camera. Proc. of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2019). Miami, FL, 4116–4129. https://doi.org/10.33012/2019.17117
↵
1. Bhamidipati, S., &
2. Gao, G. X.
(2020). Integrity monitoring of Graph-SLAM using GPS and fish-eye camera. NAVIGATION, 67(3), 583–600. https://doi.org/10.1002/navi.381
↵
1. Blanch, J.,
2. Ene, A.,
3. Walter, T., &
4. Enge, P.
(2007). An optimized multiple hypothesis RAIM algorithm for vertical guidance. Proc. of the 20th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2007), Fort Worth, TX, 2924–2933. https://www.ion.org/publications/abstract.cfm?articleID=7644
↵
1. Blanch, J.,
2. Walker, T.,
3. Enge, P.,
4. Lee, Y.,
5. Pervan, B.,
6. Rippl, M.,
7. Spletter, A., &
8. Kropp, V.
(2015). Baseline advanced RAIM user algorithm and possible improvements. IEEE Transactions on Aerospace and Electronic Systems, 51(1), 713–732. https://doi.org/10.1109/taes.2014.130739
↵
1. Bulusu, S.,
2. Kailkhura, B.,
3. Li, B.,
4. Varshney, P. K., &
5. Song, D.
(2020). Anomalous instance detection in deep learning: A survey. 42nd IEEE Symposium on Security and Privacy. Princeton, NJ. https://www.osti.gov/servlets/purl/1631092
↵
1. Cadena, C.,
2. Carlone, L.,
3. Carrillo, H.,
4. Latif, Y.,
5. Scaramuzza, D.,
6. Neira, J.,
7. Reid, I., &
8. Leonard, J. J.
(2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/tro.2016.2624754
↵
1. Calhoun, S. M., &
2. Raquet, J.
(2016). Integrity determination for a vision based precision relative navigation system. 2016 IEEE/ION Position, Location, and Navigation Symposium (PLANS), Savannah, GA. https://doi.org/10.1109/plans.2016.7479713
↵
1. Calhoun, S. M.,
2. Raquet, J., &
3. Peterson, G.
(2015). Vision-aided integrity monitor for precision relative navigation systems. Proc. of the 2015 International Technical Meeting of the Institute of Navigation, Dana Point, CA, 756–767. https://www.ion.org/publications/abstract.cfm?articleID=12668
↵
1. Campos, C.,
2. Elvira, R.,
3. Rodríguez, J. J. G.,
4. Montiel, J. M. M., &
5. Tardós, J. D.
(2021). ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimap SLAM. IEEE Transactions on Robotics, 37(6), 1874–1890. https://doi.org/10.1109/tro.2021.3075644
↵
1. Chen, C.,
2. Wang, B.,
3. Lu, C. X.,
4. Trigoni, N., &
5. Markham, A.
(2020). A survey on deep learning for localization and mapping: Towards the age of spatial machine intelligence. https://www.arxiv-vanity.com/papers/2006.12567
↵
1. Cvišić, I.,
2. Ćesić, J.,
3. Marković, I., &
4. Petrović, I.
(2017). SOFT-SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles. Journal of Field Robotics, 35(4), 578–595. https://doi.org/10.1002/rob.21762
↵
1. Delmerico, J.,
2. Cieslewski, T.,
3. Rebecq, H.,
4. Faessler, M., &
5. Scaramuzza, D.
(2019). Are we ready for autonomous drone racing? the UZH-FPV drone racing dataset. 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada. https://doi.org/10.1109/icra.2019.8793887
↵
1. DeTone, D.,
2. Malisiewicz, T., &
3. Rabinovich, A.
(2018). Superpoint: Self-supervised interest point detection and description. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, 224–236. https://doi.org/10.1109/cvprw.2018.00060
↵
1. Engel, J.,
2. Koltun, V., &
3. Cremers, D.
(2018). Direct sparse odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), 611–625. https://doi.org/10.1109/tpami.2017.2658577
↵
1. Engel, J.,
2. Schöps, T., &
3. Cremers, D.
(2014). LSD-SLAM: Large-Scale Direct Monocular SLAM. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision – ECCV 2014. Lecture Notes in Computer Science (Vol 8690, pp. 834–849). Springer. https://doi.org/10.1007/978-3-319-10605-2_54
↵
European Space Agency. (2011). Integrity. Navipedia. https://gssc.esa.int/navipedia/index.php/Integrity
↵
EUSPA. (2021a). Report on aviation user needs and requirements (Report No. EUSPA-MKD-AV-UREQ-250287). European Union Agency for the Space Programme. https://www.gsc-europa.eu/sites/default/files/sites/all/files/Report_on_User_Needs_and_Requirements_Aviation.pdf
↵
EUSPA. (2021b). Report on rail user needs and requirements (Report No. GSA-MKD-RL-UREQ-250286). European Union Agency for the Space Programme. https://www.gsc-europa.eu/sites/default/files/sites/all/files/Report_on_User_Needs_and_Requirements_Rail.pdf
↵
EUSPA. (2021c). Report on road user needs and requirements (Report No. GSA-MKD-RD-UREQ-250283). European Union Agency for the Space Programme. https://www.gsc-europa.eu/sites/default/files/sites/all/files/Report_on_User_Needs_and_Requirements_Road.pdf
↵
1. Fischler, M. A., &
2. Bolles, R. C.
(1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. https://doi.org/10.1145/358669.358692
CrossRef Web of Science
↵
1. Fryer, J. G., &
2. Brown, D. C.
(1986). Lens distortion for close-range photogrammetry. Photogrammetric Engineering and Remote Sensing, 52(1), 51–58. https://www.asprs.org/wp-content/uploads/pers/1986journal/jan/1986_jan_51-58.pdf
Web of Science
↵
1. Fu, L.,
2. Zhang, J.,
3. Li, R.,
4. Cao, X., &
5. Wang, J.
(2015). Vision-aided RAIM: A new method for GPS integrity monitoring in approach and landing phase. Sensors, 15(9), 22854–22873. https://doi.org/10.3390/s150922854
↵
1. Gao, X.,
2. Wang, R.,
3. Demmel, N., &
4. Cremers, D.
(2018). LDSO: Direct sparse odometry with loop closure. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2198–2204. https://doi.org/10.1109/IROS.2018.8593376
↵
1. Geiger, A.,
2. Lenz, P., &
3. Urtasun, R.
(2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI. https://doi.org/10.1109/cvpr.2012.6248074
↵
1. Hartley, R., &
2. Zisserman, A.
(2003). Multiple view geometry in computer vision. Cambridge University Press. https://doi.org/10.1017/cbo9780511811685
↵
1. Heikkila, J., &
2. Silven, O.
(1997). A four-step camera calibration procedure with implicit image correction. Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rick, 1106–1112. https://doi.org/10.1109/cvpr.1997.609468
↵
1. Joerger, M.,
2. Chan, F.-C., &
3. Pervan, B.
(2014). Solution separation versus residual-based RAIM. NAVIGATION, 61(4), 273–291. https://doi.org/10.1002/navi.71
↵
1. Kaess, M.,
2. Johannsson, H.,
3. Roberts, R.,
4. Ila, V.,
5. Leonard, J. J., &
6. Dellaert, F.
(2011). iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, 31(2), 216–235. https://doi.org/10.1177/0278364911430419
Web of Science
↵
1. Kendall, A., &
2. Cipolla, R.
(2016). Modelling uncertainty in deep learning for camera relocalization. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 4762–4769. https://doi.org/10.1109/icra.2016.7487679
↵
1. Kendall, A.,
2. Grimes, M., &
3. Cipolla, R.
(2015). PoseNet: A convolutional network for real-time 6-DOF camera relocalization. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2938–2946. https://doi.org/10.1109/iccv.2015.336
↵
1. Kumar, A.,
2. Braud, T.,
3. Tarkoma, S., &
4. Hui, P.
(2020). Trustworthy AI in the age of pervasive computing and big data. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX. https://doi.org/10.1109/percomworkshops48775.2020.9156127
↵
1. Langley, R. B.
(1999). The integrity of GPS. GPS World, 10(3), 60–63. http://www2.unb.ca/gge/Resources/gpsworld.march99.pdf
↵
1. Lepetit, V.,
2. Moreno-Noguer, F., &
3. Fua, P.
(2009). EPnP: An accurate o(n) solution to the PnP problem. International Journal of Computer Vision, 81(2). https://doi.org/10.1007/s11263-008-0152-6
↵
1. Liu, W.,
2. Wang, Z.,
3. Liu, X.,
4. Zeng, N.,
5. Liu, Y., &
6. Alsaadi, F. E.
(2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11–26. https://doi.org/10.1016/j.neucom.2016.12.038
↵
1. Lowe, D. G.
(2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/b:visi.0000029664.99615.94
CrossRef
↵
1. Lu, X. X.
(2018). A review of solutions for perspective-n-point problem in camera pose estimation. Journal of Physics: Conference Series, 1087(5). https://doi.org/10.1088/1742-6596/1087/5/052009
↵
1. Lynen, S.,
2. Zeisl, B.,
3. Aiger, D.,
4. Bosse, M.,
5. Hesch, J.,
6. Pollefeys, M.,
7. Siegwart, R., &
8. Sattler, T.
(2020). Large-scale, real-time visual–inertial localization revisited. The International Journal of Robotics Research, 39(9), 1061–1084. https://doi.org/10.1177/0278364920931151
↵
1. Mario, C., &
2. Rife, J.
(2010). Integrity monitoring of vision-based automotive lane detection methods. Proc. of the 23rd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2010), Portland, OR, 245–255. https://www.ion.org/publications/abstract.cfm?articleID=9152
↵
1. Meer, P.,
2. Mintz, D.,
3. Rosenfeld, A., &
4. Kim, D. Y.
(1991). Robust regression methods for computer vision: A review. International Journal of Computer Vision, 6(1), 59–70. https://doi.org/10.1007/bf00127126
↵
1. Mur-Artal, R., &
2. Tardós, J. D.
(2017). ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 33(5), 1255–1262. https://doi.org/10.1109/TRO.2017.2705103
↵
1. Polic, M.,
2. Steidl, S.,
3. Albl, C.,
4. Kukelova, Z., &
5. Pajdla, T.
(2020). Uncertainty based camera model selection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA. https://doi.org/10.1109/cvpr42600.2020.00603
↵
1. Qin, T.,
2. Li, P., &
3. Shen, S.
(2018). VINS-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004–1020. https://doi.org/10.1109/tro.2018.2853729
↵
1. Radwan, N.,
2. Valada, A., &
3. Burgard, W.
(2018). VLocNet++: Deep multitask learning for semantic visual localization and odometry. IEEE Robotics and Automation Letters, 3(4), 4407–4414. https://doi.org/10.1109/lra.2018.2869640
↵
RTCA. (2004). Minimum aviation system performance standards for local area augmentation system (Report No. RTCA DO-245).
↵
1. Rublee, E.,
2. Rabaud, V.,
3. Konolige, K., &
4. Bradski, G.
(2011). ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision, Barcelona, Spain. https://doi.org/10.1109/iccv.2011.6126544
↵
1. Sattler, T.,
2. Zhou, Q.,
3. Pollefeys, M., &
4. Leal-Taixé, L.
(2019). Understanding the limitations of CNN-based absolute camera pose regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA. https://doi.org/10.1109/cvpr.2019.00342
↵
1. Seibold, C.,
2. Hilsmann, A., &
3. Eisert, P.
(2017). Model-based motion blur estimation for the improvement of motion tracking. Computer Vision and Image Understanding, 160, 45–56. https://doi.org/10.1016/j.cviu.2017.03.005
↵
1. Shi, J., &
2. Tomasi, C.
(1994). Good features to track. Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA. https://doi.org/10.1109/cvpr.1994.323794
↵
1. Shytermeja, E.,
2. Garcia-Pena, A., &
3. Julien, O.
(2014). Proposed architecture for integrity monitoring of a GNSS/MEMS system with a fisheye camera in urban environment. International Conference on Localization and GNSS, Helsinki, Finland. https://doi.org/10.1109/ICL-GNSS.2014.6934179
↵
1. Sinha, A.,
2. Namkoong, H.,
3. Volpi, R., &
4. Duchi, J.
(2018). Certifying some distributional robustness with principled adversarial training. 6th International Conference on Learning Representations, Vancouver, Canada.
↵
1. Sturm, P.,
2. Ramalingam, S.,
3. Tardif, J.-P.,
4. Gasparini, S., &
5. Barreto, J. a.
(2011). Camera models and fundamental concepts used in geometric computer vision, 6(1–2). https://doi.org/10.1561/0600000023
↵
1. Tossaint, M.,
2. Samson, J.,
3. Toran, F.,
4. Ventura-Traveset, J.,
5. Hernandez-Pajares, M.,
6. Juan, J. M.,
7. Sanz, J., &
8. Ramos-Bosch, P.
(2007). The Stanford – ESA integrity diagram: A new tool for the user domain SBAS integrity assessment. NAVIGATION, 54(2), 153–162. https://doi.org/10.1002/j.2161-4296.2007.tb00401.x
↵
1. Wang, S.,
2. Zhan, X.,
3. Fu, Y., &
4. Zhai, Y.
(2020). Feature-based visual navigation integrity monitoring for urban autonomous platforms. Aerospace Systems, 3(3), 167–179. https://doi.org/10.1007/s42401-020-00057-8
↵
1. Yang, C.,
2. Vadlamani, A.,
3. Soloviev, A.,
4. Veth, M., &
5. Taylor, C.
(2018). Feature matching error analysis and modeling for consistent estimation in vision-aided navigation. NAVIGATION, 65(4), 609–628. https://doi.org/10.1002/navi.265
↵
1. Zhang, Z.
(2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718
CrossRef Web of Science
↵
1. Zhao, H.,
2. Shi, Y.,
3. Tong, X.,
4. Ying, X., &
5. Zha, H.
(2020). A simple yet effective pipeline for radial distortion correction. 2020 IEEE International Conference on Image Processing (ICIP), Aby Dhabi, United Arab Emirates. https://doi.org/10.1109/icip40778.2020.9191107
↵
1. Zhou, L., &
2. Kaess, M.
(2019). An efficient and accurate algorithm for the perspecitve-n-point problem. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China. https://doi.org/10.1109/iros40897.2019.8968482
↵
1. Zhou, Q.,
2. Sattler, T.,
3. Pollefeys, M., &
4. Leal-Taixé, L.
(2020). To learn or not to learn: Visual localization from essential matrices. 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France. https://doi.org/10.1109/icra40945.2020.9196607
1. Zhu, C.
(2020). Cooperative vision for swarm navigation [Doctoral dissertation, Technische Universität München]. https://mediatum.ub.tum.de/doc/1486567/1486567.pdf
↵
1. Zhu, C.,
2. Giorgi, G., &
3. Günther, C.
(2017). Planar pose estimation using a camera and single-station ranging measurements. Proc. of the 30th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, OR. https://doi.org/10.33012/2017.15219
↵
1. Zhu, C.,
2. Giorgi, G., &
3. Günther, C.
(2018). 2D relative pose and scale estimation with monocular cameras and ranging. NAVIGATION, 65(1), 25–33. https://doi.org/10.1002/navi.223
↵
1. Zhu, C.,
2. Joerger, M., &
3. Meurer, M.
(2020). Quantifying feature association error in camera-based positioning. 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR. https://doi.org/10.1109/plans46316.2020.9109919
↵
1. Zhu, C.,
2. Steinmetz, C.,
3. Belabbas, B., &
4. Meurer, M.
(2019a). Feature error model for integrity of pattern-based visual positioning. Proc. of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2019), Miami, FL, 2254–2268. https://doi.org/10.33012/2019.16956
↵
1. Zhu, C.,
2. Steinmetz, C.,
3. Belabbas, B., &
4. Meurer, M.
(2019b). Six degrees-of-freedom dilution of precision for integrity of camera-based localization. Proc. of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2019), Miami, FL. https://doi.org/10.33012/2019.17020
1. Zhu, Z., &
2. Taylor, C.
(2017). Conservative uncertainty estimation in map-based vision-aided navigation. IEEE Transactions on Aerospace and Electronic Systems, 53(2), 941–949. https://doi.org/10.1109/taes.2017.2667278

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