activity
20192026
most citedAdaptive Continuous Visual Odometry from RGB-D Images

5 citations · 12 across the 15 of their papers we have counts for

collaborators

15 papers

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.LG2025

Equivariant Neural Networks for General Linear Symmetries on Lie Algebras

Chankyo Kim, Sicheng Zhao, Minghan Zhu +2

Many scientific and geometric problems exhibit general linear symmetries, yet most equivariant neural networks are built for compact groups or simple vector features, limiting thei…

cs.RO2025

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,…

cs.RO2025

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…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2023★ 1 cited

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…

cs.LG2023

Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras

Tzu-Yuan Lin, Minghan Zhu, Maani Ghaffari

This paper proposes an equivariant neural network that takes data in any semi-simple Lie algebra as input. The corresponding group acts on the Lie algebra as adjoint operations, ma…

cs.RO2022

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…

cs.RO2021★ 3 cited

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…

cs.CV2020★ 1 cited

A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras

Ray Zhang, Tzu-Yuan Lin, Chien Erh Lin +5

This paper reports on a novel nonparametric rigid point cloud registration framework that jointly integrates geometric and semantic measurements such as color or semantic labels in…

cs.RO2019★ 2 cited

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…

cs.RO2019★ 5 cited

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…