Experimental evaluation of uncalibrated visual servoing for precision manipulation

By Martin Jägersand, Olac Fuentes, R. Nelson. 1997

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In Robotics & Control

Citations in the corpus

End-to-End Training of Deep Visuomotor Policies (2015, neutral): “Furthermore, our approach does not require any sort of camera calibration, in contrast to many visual servoing methods (though not all – see e.g. [11, 43]).”
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “, 2013), and several visual servoing methods have been proposed that do not directly require prior calibration between the robot and camera (Hosoda and Asada, 1994; Jägersand et al., 1997; Kragic and Christensen, 2002; Yoshimi and Allen, 1994).”
Deep spatial autoencoders for visuomotor learning (2015, neutral): “Furthermore, our approach does not require any sort of camera calibration, in contrast to many visual servoing methods (though not all – see e.g. [27], [28]).”
Survey on Visual Servoing for Manipulation (2002, methodology): “1999) 3 IB VM2 (Jang & Bien 1991) 3 IB VM1 (Jarabek & Capson 1998) 2 IB VM2 (Joshi & Sanderson 1996) 5 IB VM4 (Jägersand et al. 1997) 3-12 EA VM4”
Uncalibrated visual servoing of robots using a depth-independent interaction matrix (2006, methodology): “For the fixed camera configuration, Jagersand et al. [19] proposed a similar method to that in [11], [16].”
Time Reversal as Self-Supervision (2018, background): “While some visual servoing methods work with uncalibrated camera inputs [9], [10], the general hand crafted nature of visual servoing limits the complexity of visual inputs and tasks where it can be applied.”
Sim2Real View Invariant Visual Servoing by Recurrent Control (2017, unknown): “Several prior visual servoing methods also address servoing with uncalibrated cameras [36, 16, 21], but all of them address an “eyein-hand” setting, where the goal is to servo the camera toward a target view by using previously known geometric features and estimate the image Jacobian within an image”
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders (2015, result): “Furthermore, our approach does not require any sort of camera calibration, in contrast to many visual servoing methods (though not all – see e.g. [27], [28]).”
Sim2Real Viewpoint Invariant Visual Servoing by Recurrent Control (2018, background): “Several prior visual servoing methods also address servoing with uncalibrated cameras [36, 16, 21], but all of them address an “eye-inhand” setting, where the goal is to servo the camera toward a target view by using previously known geometric features and estimate the image Jacobian within an image”