Search Results for author:
Found 9 papers, 6 papers with code
The Sum of Its Parts: Visual Part Segmentation for Inertial Parameter Identification of Manipulated Objects
To operate safely and efficiently alongside human workers, collaborative robots (cobots) require the ability to quickly understand the dynamics of manipulated objects.
A Question of Time: Revisiting the Use of Recursive Filtering for Temporal Calibration of Multisensor Systems
We examine the problem of time delay estimation, or temporal calibration, in the context of multisensor data fusion.
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation.
Certifiably Optimal Monocular Hand-Eye Calibration
Correct fusion of data from two sensors is not possible without an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration.
Robotics
Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization
This is accomplished through a novel formulation of inverse kinematics as a nearest point problem, and with a fast sum of squares solver that exploits the sparsity of kinematic constraints for serial manipulators.
Robotics Optimization and Control
Sparse Bounded Degree Sum of Squares Optimization for Certifiably Globally Optimal Rotation Averaging
Estimating unknown rotations from noisy measurements is an important step in SfM and other 3D vision tasks.
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics.
Certifiably Globally Optimal Extrinsic Calibration from Per-Sensor Egomotion
We present a certifiably globally optimal algorithm for determining the extrinsic calibration between two sensors that are capable of producing independent egomotion estimates.
Robotics
PROBE: Predictive Robust Estimation for Visual-Inertial Navigation
Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community.
