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UID:6c3687a44119db3d11243486b5515205
DTSTAMP:20260513T132029Z
SUMMARY:SLAM Techniques for Automatically Tracking and Calibrating Robot Ar
 ms without Fiducials
DESCRIPTION:Abstract:\nRobot manipulation remains a challenging task. One o
 f the key problems is uncertainty of the robot’s kinematic parameters su
 ch as its joint angles and the extrinsic calibration of its sensors with r
 espect to its body. Even when the robot is properly calibrated\, unknown d
 ynamics and underactuated degrees of freedom introduce errors that can cau
 se it to totally fail at common manipulation tasks. Can the robot correct 
 for these problems automatically just using its sensors? We propose a meth
 od based on Bayesian graph SLAM that uses a monocular RGB camera mounted o
 n the robot arm’s end effector to automatically calibrate and track it w
 ithout the use of special markers or fiducials. We show that with our meth
 od\, we are able to recover key parameters of the robot and their uncertai
 nties in real time as the robot moves. We also provide analysis of how the
  robot’s structure and parameters of its sensor affect the SLAM system
 ’s ability to track and calibrate it.
URL:https://www.imperial.ac.uk/events/102281/slam-techniques-for-automatica
 lly-tracking-and-calibrating-robot-arms-without-fiducials/
DTSTART;TZID=Europe/London:20160511T140000
DTEND;TZID=Europe/London:20160511T150000
LOCATION:United Kingdom
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