Search Results for author: Jessy W. Grizzle

Found 13 papers, 11 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.

Object Tracking Pose Estimation +3

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.

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

1 code implementation10 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.

Point Cloud Registration

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 +1

Adaptive Continuous Visual Odometry from RGB-D Images

1 code implementation1 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.

Visual Odometry

Bayesian Spatial Kernel Smoothing for ScalableDense Semantic Mapping

2 code implementations10 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.


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.

Object Tracking Simultaneous Localization and Mapping +1

Contact-Aided Invariant Extended Kalman Filtering for Robot State Estimation

1 code implementation19 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.


Rapid Trajectory Optimization Using C-FROST with Illustration on a Cassie-Series Dynamic Walking Biped

1 code implementation17 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

Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation

2 code implementations26 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.


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.


Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors

no code implementations15 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.


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