SyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable and
Overview
This research introduces SyncHeC, a framework for markerless hand-eye calibration that jointly estimates camera geometry and time offsets from continuous robot motion using differentiable rendering. Evaluated on a 7-DoF arm, the method achieves 0.21 cm translation error and 2.7 ms latency error against reference benchmarks, outperforming CM-Calib by 3.7x in translation and 2.3x in latency. The approach supports Eye-to-Hand, Eye-in-Hand, and multi-camera setups across seven arms and four depth sensors, with policy training integration showing an 8.1 percentage point increase in success rates across six task families.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · translation error: 0.21 other · Basis not reported
“ic depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s motion yields $0.21$\,cm translation error against a marker-board reference, $0.34$\,cm touch-test error, and $2.7$\,ms latency error relative to an $87$\,ms reference with $95\%$ uncertainty o”
Exact source · revision 1Source owner not reported
Reported quantity · touch-test error: 0.34 other · Basis not reported
“ic depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s motion yields $0.21$\,cm translation error against a marker-board reference, $0.34$\,cm touch-test error, and $2.7$\,ms latency error relative to an $87$\,ms reference with $95\%$ uncertainty o”
Exact source · revision 1Source owner not reported
Reported quantity · latency error: 2.7 other · Basis not reported
“ic depth complements mask contours, and an offline planner combines spatial sensitivity with temporal excitation. Alternating updates support Eye-to-Hand, Eye-in-Hand, and multi-camera calibration. On a 7-DoF arm, one $6.5$\,s motion yields $0.21$\,cm translation error against a marker-board reference, $0.34$\,cm touch-test error, and $2.7$\,ms latency error relative to an $87$\,ms reference with $95\%$ uncertainty o”
Exact source · revision 1Source owner not reported
Reported quantity · translation error improvement factor: 3.7 other · Basis not reported
“f $\pm1.5$\,ms. Against CM-Calib on matched recordings, translation and nominal latency errors improve by $3.7\times$ and $2.3\times$. Experiments span seven arms and four depth sensors. Compensating the recovered offset during policy training and deployment increases success by $8.1$ percentage points across six families and six tasks ($1{,}800$ matched pairs; paired $95\%$ confidence interval $[6.3,9.9]$ points).”
Exact source · revision 1Source owner not reported
Reported quantity · latency error improvement factor: 2.3 other · Basis not reported
“f $\pm1.5$\,ms. Against CM-Calib on matched recordings, translation and nominal latency errors improve by $3.7\times$ and $2.3\times$. Experiments span seven arms and four depth sensors. Compensating the recovered offset during policy training and deployment increases success by $8.1$ percentage points across six families and six tasks ($1{,}800$ matched pairs; paired $95\%$ confidence interval $[6.3,9.9]$ points).”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSyncHeC: Markerless Spatio-Temporal Hand-Eye Calibration via Differentiable Rendering and Continuous-Time Joint Optimization
This research introduces SyncHeC, a framework for markerless hand-eye calibration that jointly estimates camera geometry and time offsets from continuous robot motion using differentiable rendering. Evaluated on a 7-DoF arm, the method achieves 0.21 cm translation error and 2.7 ms latency error against reference benchmarks, outperforming CM-Calib by 3.7x in translation and 2.3x in latency. The approach supports Eye-to-Hand, Eye-in-Hand, and multi-camera setups across seven arms and four depth sensors, with policy training integration showing an 8.1 percentage point increase in success rates across six task families.
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