Search Results for author: Jiunn-Kai Huang

Found 6 papers, 5 papers with code

Optimal Target Shape for LiDAR Pose Estimation

3 code implementations2 Sep 2021 Jiunn-Kai Huang, William Clark, Jessy W. Grizzle

However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR.

Motion Capture Object Tracking +4

Global Unifying Intrinsic Calibration for Spinning and Solid-State LiDARs

5 code implementations6 Dec 2020 Jiunn-Kai Huang, Chenxi Feng, Madhav Achar, Maani Ghaffari, Jessy W. Grizzle

By modeling the calibration parameters as an element of a special matrix Lie Group, we achieve a unifying view of calibration for different types of LiDARs.

Improvements to Target-Based 3D LiDAR to Camera Calibration

6 code implementations7 Oct 2019 Jiunn-Kai Huang, Jessy W. Grizzle

The homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM.

Pose Estimation Quantization +2

LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

4 code implementations23 Aug 2019 Jiunn-Kai Huang, Shoutian Wang, Maani Ghaffari, Jessy W. Grizzle

Because of the LiDAR sensors' nature, rapidly changing ambient lighting will not affect the detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment.

Motion Capture Object Tracking +2

Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway

1 code implementation19 Sep 2018 Yukai Gong, Ross Hartley, Xingye Da, Ayonga Hereid, Omar Harib, Jiunn-Kai Huang, Jessy Grizzle

The Cassie bipedal robot designed by Agility Robotics is providing academics a common platform for sharing and comparing algorithms for locomotion, perception, and navigation.

Robotics Optimization and Control

Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs

no code implementations20 Mar 2018 Ross Hartley, Maani Ghaffari Jadidi, Lu Gan, Jiunn-Kai Huang, Jessy W. Grizzle, Ryan M. Eustice

The factor graph framework is a convenient modeling technique for robotic state estimation where states are represented as nodes, and measurements are modeled as factors.


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