{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T09:21:07Z","timestamp":1778923267759,"version":"3.51.4"},"reference-count":42,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2004,12,1]],"date-time":"2004-12-01T00:00:00Z","timestamp":1101859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2004,12]]},"abstract":"<jats:p>Cameras and inertial sensors are each good candidates for autonomous vehicle navigation, modeling from video, and other applications that require six-degrees-of-freedom motion estimation. However, these sensors are also good candidates to be deployed together, since each can be used to resolve the ambiguities in estimated motion that result from using the other modality alone. In this paper, we consider the specific problem of estimating sensor motion and other unknowns from image, gyro, and accelerometer measurements, in environments without known fiducials. This paper targets applications where external positions references such as global positioning are not available, and focuses on the use of small and inexpensive inertial sensors, for applications where weight and cost requirements preclude the use of precision inertial navigation systems.<\/jats:p>\n                  <jats:p>We present two algorithms for estimating sensor motion from image and inertial measurements. The first algorithm is a batch method, which produces estimates of the sensor motion, scene structure, and other unknowns using measurements from the entire observation sequence simultaneously. The second algorithm recovers sensor motion, scene structure, and other parameters recursively, and is suitable for use with long or \u201cinfinite\u201d sequences, in which no feature is always visible.<\/jats:p>\n                  <jats:p>We evaluate the accuracy of the algorithms and their sensitivity to their estimation parameters using a sequence of four experiments. These experiments focus on cases where estimates from image or inertial measurements alone are poor, on the relative advantage of using inertial measurements and omni directional images, and on long sequences in which the percentage of the image sequence in which individual features are visible is low.<\/jats:p>","DOI":"10.1177\/0278364904045593","type":"journal-article","created":{"date-parts":[[2004,12,2]],"date-time":"2004-12-02T07:48:42Z","timestamp":1101973722000},"page":"1157-1195","source":"Crossref","is-referenced-by-count":92,"title":["Motion Estimation from Image and Inertial Measurements"],"prefix":"10.1177","volume":"23","author":[{"given":"Dennis","family":"Strelow","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA 15213, USA,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjiv","family":"Singh","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2004,12,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"crossref","unstructured":"Baker, S., and Nayar, S.K. 2001. Single viewpoint catadioptric cameras. Panoramic Vision: Sensors, Theory, and Applications. Springer-Verlag, New York , pp. 39-71.","DOI":"10.1007\/978-1-4757-3482-9_4"},{"key":"atypb2","unstructured":"Bar-Shalom, Y., and Li, X.R. 1995. Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs, CT ."},{"key":"atypb3","unstructured":"Brooks, M.J., Chojnacki, W., Gawley, D., and van den Hengel, A. 2001. What value covariance information in estimating vision parameters? Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV 2001), Vancouver, Canada."},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1364\/AO.36.008275"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1162\/105474602320935829"},{"key":"atypb6","doi-asserted-by":"crossref","unstructured":"Chong, K.S., and Kleeman, L. 1999. Feature-based mapping in real, large scale environments using an ultrasonic array . International Journal of Robotics Research 18(1): 3-19 .","DOI":"10.1177\/027836499901800101"},{"key":"atypb7","unstructured":"Craig, J.J. 1989. Introduction to Robotics: Mechanics and Control. Addison-Wesley, Reading, MA ."},{"key":"atypb8","unstructured":"Deans, M.C. 2002.\n                      Bearings-Only Localization and Mapping\n                      . PhD thesis, Carnegie Mellon University, Pittsburgh, PA."},{"key":"atypb9","doi-asserted-by":"crossref","unstructured":"Deans, M., and Hebert, M. 2001. Experimental comparison of techniques for localization and mapping using a bearing-only sensor. Experimental Robotics VII, D. Rus and S. Singh, editors. Springer-Verlag, Berlin , pp. 395-404.","DOI":"10.1007\/3-540-45118-8_40"},{"key":"atypb10","unstructured":"Foxlin, E.M. 2002. Generalized architecture for simultaneous localization, auto-calibration, and map-building . Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), EPFL, Lausanne, Switzerland, September 30-October 4."},{"key":"atypb11","unstructured":"Foxlin, E., and Naimark, L. 2003. VIS-Tracker: a wearable vision-inertial self-tracker . IEEE Virtual Reality Conference (VR 2003), Los Angeles, CA, March."},{"key":"atypb12","unstructured":"Gelb, A., editor. 1974. Applied Optimal Estimation. MIT Press, Cambridge, MA ."},{"key":"atypb13","unstructured":"Guivant, J., and Nebot, E. 2001. Compressed filter for real time implementation of simultaneous localization and mapping . International Conference on Field and Service Robotics (FSR 2001), Otaniemi, Finland, June, Vol. 1, pp. 309-314 ."},{"key":"atypb14","unstructured":"Heikkil\u00e4, J., and Silv\u00e9n, O. 1997. A four-step camra calibration procedure with implicit image correction . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1997), San Juan, Puerto Rico, pp. 1106-1112 ."},{"key":"atypb15","doi-asserted-by":"crossref","unstructured":"Horn, B.K.P. 1987. Closed-form solution of absolute orientation using unit quaternions . Journal of the Optical Society of America A 4(4): 629-642 .","DOI":"10.1364\/JOSAA.4.000629"},{"key":"atypb16","unstructured":"Huster, A., and Rock, S.M. 2001a. Relative position estimation for intervention-capable AUVs by fusing vision and inertial measurements . Proceedings of the 12th International Symposium on Unmanned Untethered Submersible Technology, Durham, NH, August."},{"key":"atypb17","doi-asserted-by":"crossref","unstructured":"Huster, A., and Rock, S.M. 2001b. Relative position estimation for manipulation tasks by fusing vision and inertial measurements . Oceans 2001 Conference, Honolulu, HI, November, Vol. 2, pp. 1025-1031 .","DOI":"10.1109\/OCEANS.2001.968257"},{"key":"atypb18","unstructured":"Huster, A., and Rock, S.M. 2003. Relative position sensing by fusing monocular vision and inertial rate sensors . Proceedings of the 11th International Conference on Advanced Robotics (ICAR 2003), Coimbra, Portugal, July, Vol. 3, pp. 1562-1567 ."},{"key":"atypb19","unstructured":"Huster, A., Frew, E.W., and Rock, S.M. 2002. Relative position estimation for AUVs by fusing bearing and inertial rate sensor measurements . Oceans 2002 Conference, Biloxi, MS, October, pp. 1857-1864 ."},{"key":"atypb20","doi-asserted-by":"crossref","unstructured":"Jung, S.H., and Taylor, C.J. 2001. Camera trajectory estimation using inertial sensor measurements and structure from motion results . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, December, Vol. 2, pp. 732-737 .","DOI":"10.1109\/CVPR.2001.991037"},{"key":"atypb21","unstructured":"Kanazawa, Y., and Kanatani, K. 2001. Do we really have to consider covariance matrices for image features? Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV 2001), Vancouver, Canada, July."},{"key":"atypb22","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. 1999. Object recognition and local scale-invariant features . Proceedings of the 7th International Conference on Computer Vision (ICCV 1999), Corfu, Greece, September, pp. 1150-1157 .","DOI":"10.1109\/ICCV.1999.790410"},{"key":"atypb23","unstructured":"Lucas, B.D., and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision . Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, Canada, August, Vol. 2, pp. 674-679 ."},{"key":"atypb24","unstructured":"McLauchlan, P.F. 1999. The variable state dimension filter applied to surface-based structure from motion. Technical Report VSSP-TR-4\/99, University of Surrey, Guildford, UK ."},{"key":"atypb25","unstructured":"Montemerlo, M., and Thrun, S. 2003. Simultaneous localization and mapping with unknown data association using Fast SLAM . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2003), Taipei, Taiwan."},{"key":"atypb26","doi-asserted-by":"crossref","unstructured":"Mukai, T., and Ohnishi, N. 1999. The recovery of object shape and camera motion using a sensing system with a video camera and a gyro sensor . Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV 1999), Corfu, Greece, September, pp. 411-417 .","DOI":"10.1109\/ICCV.1999.791250"},{"key":"atypb27","doi-asserted-by":"crossref","unstructured":"Nayar, S.K. 1997. Catadioptric omnidirectional camera . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1997), San Juan, Puerto Rico, June, pp. 482-488 .","DOI":"10.1109\/CVPR.1997.609369"},{"key":"atypb28","unstructured":"Ollis, M., Herman, H., and Singh, S. 1999. Analysis and design of panoramic stereo vision using equi-angular pixel cameras. Technical Report CMU-RI-TR-99-04, Carnegie Mellon University, Pittsburgh, PA ."},{"key":"atypb29","unstructured":"Poelman, C.J. 1995.\n                      The Para perspective and Projective Factorization Methods for Recovering Shape and Motion\n                      . PhD thesis, Carnegie Mellon University, Pittsburgh, PA."},{"key":"atypb30","unstructured":"Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1992. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge ."},{"key":"atypb31","unstructured":"Qian, G., and Chellappa, R. 2001. Structure from motion using sequential Monte Carlo methods . Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV 2001), Vancouver, Canada, July, pp. 614-621 ."},{"key":"atypb32","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.18.002982"},{"key":"atypb33","doi-asserted-by":"publisher","DOI":"10.1109\/34.927458"},{"key":"atypb34","doi-asserted-by":"crossref","unstructured":"Rehbinder, H., and Ghosh, B.K. 2001. Rigid body state estimation using dynamic vision and inertial sensors . Proceedings of the 40th IEEE Conference on Decision and Control (CDC 2001), Orlando, FL, December, pp. 2398-2403 .","DOI":"10.1109\/CDC.2001.980621"},{"key":"atypb35","doi-asserted-by":"crossref","unstructured":"Smith, R., Self, M., and Cheeseman, P. 1990. Estimating uncertain spatial relationships in robotics. Autonomous Robot Vehicles, I.J. Cox and G.T. Wilfong, editors. Springer-Verlag, New York , pp. 167-193.","DOI":"10.1007\/978-1-4613-8997-2_14"},{"key":"atypb36","unstructured":"Strelow, D., Mishler, J., Singh, S., and Herman, H. 2001a. Extending shape-from-motion to non-central omnidirectional cameras . Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2001), Wailea, HI, October."},{"key":"atypb37","doi-asserted-by":"crossref","unstructured":"Strelow, D., Mishler, J., Koes, D., and Singh, S. 2001b. Precise omnidirectional camera calibration . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, HI, December, Vol. 1, pp. 689-694 .","DOI":"10.1109\/CVPR.2001.990542"},{"key":"atypb38","doi-asserted-by":"publisher","DOI":"10.1006\/jvci.1994.1002"},{"key":"atypb39","doi-asserted-by":"publisher","DOI":"10.1007\/BF00129684"},{"key":"atypb40","doi-asserted-by":"publisher","DOI":"10.1109\/34.589205"},{"key":"atypb41","unstructured":"Wolf, P.R. 1983. Elements of Photogrammetry. McGraw-Hill, New York ."},{"key":"atypb42","unstructured":"You, S., and Neumann, U. 2001. Fusion of vision and gyro tracking for robust augmented reality registration . Proceedings of the IEEE Virtual Reality Conference (VR 2001), Yokohama, Japan, March, pp. 71-78 ."}],"container-title":["The International Journal of Robotics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0278364904045593","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0278364904045593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:17:04Z","timestamp":1777457824000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0278364904045593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,12]]},"references-count":42,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2004,12]]}},"alternative-id":["10.1177\/0278364904045593"],"URL":"https:\/\/doi.org\/10.1177\/0278364904045593","relation":{},"ISSN":["0278-3649","1741-3176"],"issn-type":[{"value":"0278-3649","type":"print"},{"value":"1741-3176","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,12]]}}}