5 citations · 12 across the 15 of their papers we have counts for
12 papers · 1 filter
GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry
Chankyo Kim, Minghan Zhu, Tzu-Yuan Lin +2
Neural inertial odometry increasingly uses networks as learned measurements inside filtering pipelines. Such measurements should transform consistently under arbitrary IMU mounting…
LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies
Sangli Teng, Ruiqi Zhang, Tzu-Yuan Lin +5
Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. While direct methods are widely used, the existing constrained optimizers typical…
A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
I-Chia Chang, Xinyan Huang, Tzu-Yuan Lin +5
Legged robots have demonstrated remarkable agility on rigid, stationary ground, but their locomotion reliability remains limited in non-inertial environments, where the supporting…
Riemannian Direct Trajectory Optimization of Rigid Bodies on Matrix Lie Groups
Sangli Teng, Tzu-Yuan Lin, William A Clark +2
Designing dynamically feasible trajectories for rigid bodies is a fundamental problem in robotics. Although direct trajectory optimization is widely applied to solve this problem,…
Debiasing 6-DOF IMU via Hierarchical Learning of Continuous Bias Dynamics
Ben Liu, Tzu-Yuan Lin, Wei Zhang +1
This paper develops a deep learning approach to the online debiasing of IMU gyroscopes and accelerometers. Most existing methods rely on implicitly learning a bias term to compensa…
Tensegrity Robot Proprioceptive State Estimation with Geometric Constraints
Wenzhe Tong, Tzu-Yuan Lin, Jonathan Mi +3
Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces c…
Legged Robot State Estimation within Non-inertial Environments
Zijian He, Sangli Teng, Tzu-Yuan Lin +2
This paper investigates the robot state estimation problem within a non-inertial environment. The proposed state estimation approach relaxes the common assumption of static ground…
Proprioceptive Invariant Robot State Estimation
Tzu-Yuan Lin, Tingjun Li, Wenzhe Tong +1
This paper reports on developing a real-time invariant proprioceptive robot state estimation framework called DRIFT. A didactic introduction to invariant Kalman filtering is provid…
Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer
Xihang Yu, Sangli Teng, Theodor Chakhachiro +7
This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully propri…
Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events
Tzu-Yuan Lin, Ray Zhang, Justin Yu +1
This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unli…
A Keyframe-based Continuous Visual SLAM for RGB-D Cameras via Nonparametric Joint Geometric and Appearance Representation
Xi Lin, Dingyi Sun, Tzu-Yuan Lin +2
This paper reports on a robust RGB-D SLAM system that performs well in scarcely textured and structured environments. We present a novel keyframe-based continuous visual odometry t…
Adaptive Continuous Visual Odometry from RGB-D Images
Tzu-Yuan Lin, William Clark, Ryan M. Eustice +3
In this paper, we extend the recently developed continuous visual odometry framework for RGB-D cameras to an adaptive framework via online hyperparameter learning. We focus on the…