3 code implementations • 2 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.
5 code implementations • 6 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.
1 code implementation • 10 Nov 2020 • Ray Zhang, Tzu-Yuan Lin, Chien Erh Lin, Steven A. Parkison, William Clark, Jessy W. Grizzle, Ryan M. Eustice, Maani Ghaffari
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 into the alignment process and does not require explicit data association.
7 code implementations • 7 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.
1 code implementation • 1 Oct 2019 • Tzu-Yuan Lin, William Clark, Ryan M. Eustice, Jessy W. Grizzle, Anthony Bloch, Maani Ghaffari
In this paper, we extend the recently developed continuous visual odometry framework for RGB-D cameras to an adaptive framework via online hyperparameter learning.
2 code implementations • 10 Sep 2019 • Lu Gan, Ray Zhang, Jessy W. Grizzle, Ryan M. Eustice, Maani Ghaffari
This paper develops a Bayesian continuous 3D semantic occupancy map from noisy point cloud measurements.
Robotics
4 code implementations • 23 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.
1 code implementation • 19 Apr 2019 • Ross Hartley, Maani Ghaffari, Ryan M. Eustice, Jessy W. Grizzle
This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points.
Robotics
1 code implementation • 3 Apr 2019 • Maani Ghaffari, William Clark, Anthony Bloch, Ryan M. Eustice, Jessy W. Grizzle
This paper reports on a novel formulation and evaluation of visual odometry from RGB-D images.
1 code implementation • 17 Jul 2018 • Ayonga Hereid, Omar Harib, Ross Hartley, Yukai Gong, Jessy W. Grizzle
One of the big attractions of low-dimensional models for gait design has been the ability to compute solutions rapidly, whereas one of their drawbacks has been the difficulty in mapping the solutions back to the target robot.
Robotics Systems and Control
2 code implementations • 26 May 2018 • Ross Hartley, Maani Ghaffari Jadidi, Jessy W. Grizzle, Ryan M. Eustice
On the basis of the theory of invariant observer design by Barrau and Bonnabel, and in particular, the Invariant EKF (InEKF), we show that the error dynamics of the point contact-inertial system follows a log-linear autonomous differential equation; hence, the observable state variables can be rendered convergent with a domain of attraction that is independent of the system's trajectory.
Robotics
no code implementations • 20 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.
Robotics
no code implementations • 15 Dec 2017 • Ross Hartley, Josh Mangelson, Lu Gan, Maani Ghaffari Jadidi, Jeffrey M. Walls, Ryan M. Eustice, Jessy W. Grizzle
We introduce forward kinematic factors and preintegrated contact factors into a factor graph framework that can be incrementally solved in real-time.
Robotics