{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:43:03Z","timestamp":1768776183625,"version":"3.49.0"},"reference-count":41,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2008,10,1]],"date-time":"2008-10-01T00:00:00Z","timestamp":1222819200000},"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":[[2008,10]]},"abstract":"<jats:p> In the classical image-based visual servoing framework, error signals are directly computed from image feature parameters, allowing, in principle, control schemes to be obtained that need neither a complete three-dimensional (3D) model of the scene nor a perfect camera calibration. However, when the computation of control signals involves the interaction matrix, the current value of some 3D parameters is requiredfor each considered feature, and typically a rough approximation of this value is used. With reference to the case of a point feature, for which the relevant 3D parameter is the depth Z, we propose a visual servoing approach where Z is observed and made available for servoing. This is achieved by interpreting depth as an unmeasurable state with known dynamics, and by building a non-linear observer that asymptotically recovers the actual value of Z for the selected feature. A byproduct of our analysis is the rigorous characterization of camera motions that actually allow such observation. Moreover, in the case of a partially uncalibrated camera, it is possible to exploit complementary camera motions in order to preliminarily estimate the focal length without knowing Z. Simulations and experimental results are presented for a mobile robot with an on-board camera in order to illustrate the benefits of integrating the depth observation within classical visual servoing schemes. <\/jats:p>","DOI":"10.1177\/0278364908096706","type":"journal-article","created":{"date-parts":[[2008,9,23]],"date-time":"2008-09-23T11:15:12Z","timestamp":1222168512000},"page":"1093-1116","source":"Crossref","is-referenced-by-count":152,"title":["Feature Depth Observation for Image-based Visual Servoing: Theory and                 Experiments"],"prefix":"10.1177","volume":"27","author":[{"given":"Alessandro","family":"De Luca","sequence":"first","affiliation":[{"name":"Dipartimento di Informatica e Sistemistica, Universit\u00e0 di Roma                         \"La Sapienza\", Via Ariosto 25, 00185 Roma, Italy,"}]},{"given":"Giuseppe","family":"Oriolo","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica e Sistemistica, Universit\u00e0 di Roma                         \"La Sapienza\", Via Ariosto 25, 00185 Roma, Italy,"}]},{"given":"Paolo","family":"Robuffo Giordano","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica e Sistemistica, Universit\u00e0 di Roma                         \"La Sapienza\", Via Ariosto 25, 00185 Roma, Italy,"}]}],"member":"179","published-online":{"date-parts":[[2008,10,1]]},"reference":[{"key":"atypb1","volume-title":"Proceedings of the 2006 IEEE International Conference on Robotics and Automation","author":"Benhimane, S."},{"key":"atypb2","volume-title":"Camera Calibration Toolbox for MATLAB","author":"Bouguet, J.-Y.","year":"2007"},{"key":"atypb3","doi-asserted-by":"crossref","unstructured":"Chaumette, F.                  (1998). Potential problems of stability and convergence in                     image-based and position-based visual servoing. The Confluence of Vision and                     Control,                      Kriegman, D.                 ,                      Hager, G.                  and                      Morse, A.                  (eds), Lecture Notes in Control and Information Science, Vol. 237.                         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