no code implementations • 9 Oct 2024 • Yunzhi Lin, Yipu Zhao, Fu-Jen Chu, Xingyu Chen, Weiyao Wang, Hao Tang, Patricio A. Vela, Matt Feiszli, Kevin Liang
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions.
no code implementations • 28 Mar 2023 • Hongyi Chen, Yilun Du, Yiye Chen, Joshua Tenenbaum, Patricio A. Vela
In this paper, we suggest an approach towards integrating planning with sequence models based on the idea of iterative energy minimization, and illustrate how such a procedure leads to improved RL performance across different tasks.
1 code implementation • ICCV 2023 • Yiye Chen, Yunzhi Lin, Ruinian Xu, Patricio A. Vela
The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces.
1 code implementation • 9 Mar 2023 • Yiye Chen, Ruinian Xu, Yunzhi Lin, Hongyi Chen, Patricio A. Vela
We propose a new 6-DoF grasp pose synthesis approach from 2D/2. 5D input based on keypoints.
1 code implementation • 18 Oct 2022 • Yunzhi Lin, Thomas Müller, Jonathan Tremblay, Bowen Wen, Stephen Tyree, Alex Evans, Patricio A. Vela, Stan Birchfield
We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene.
no code implementations • 11 Oct 2022 • Ahmad Abuaish, Mohit Srinivasan, Patricio A. Vela
This manuscript describes the specific geometry of the neural network used for zeroing barrier function synthesis, and shows how the network provides the necessary representation for splitting the state space into safe and unsafe regions.
1 code implementation • 23 May 2022 • Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, Stan Birchfield
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category.
1 code implementation • 13 Sep 2021 • Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio A. Vela, Stan Birchfield
Prior work on 6-DoF object pose estimation has largely focused on instance-level processing, in which a textured CAD model is available for each object being detected.
no code implementations • 16 Jun 2021 • Ruinian Xu, Fu-Jen Chu, Patricio A. Vela
Decreasing the detection difficulty by grouping keypoints into pairs boosts performance.
no code implementations • 1 Apr 2021 • Yiye Chen, Ruinian Xu, Yunzhi Lin, Patricio A. Vela
We consider the task of grasping a target object based on a natural language command query.
2 code implementations • 23 Aug 2020 • Yipu Zhao, Justin S. Smith, Patricio A. Vela
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical characteristic of resource-limited applications.
1 code implementation • 3 Mar 2020 • Yipu Zhao, Justin S. Smith, Sambhu H. Karumanchi, Patricio A. Vela
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e. g. indoor, underground.
Robotics
2 code implementations • 3 Jan 2020 • Yipu Zhao, Patricio A. Vela
Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency).
1 code implementation • 12 Sep 2019 • Yunzhi Lin, Chao Tang, Fu-Jen Chu, Patricio A. Vela
Each primitive shape is designed with parametrized grasp families, permitting the pipeline to identify multiple grasp candidates per shape primitive region.
1 code implementation • 12 Sep 2019 • Fu-Jen Chu, Ruinian Xu, Chao Tang, Patricio A. Vela
Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation.
no code implementations • 19 Aug 2019 • Alexander H. Chang, Shiyu Feng, Yipu Zhao, Justin S. Smith, Patricio A. Vela
As a first pass in this direction, we equip a wireless, monocular color camera to the head of a robotic snake.
1 code implementation • 19 May 2019 • Wenkai Ye, Yipu Zhao, Patricio A. Vela
The ad-hoc creation of these benchmarks does not necessarily illuminate the particular weak points of a SLAM algorithm when performance is evaluated.
no code implementations • 19 May 2019 • Yipu Zhao, Wenkai Ye, Patricio A. Vela
This paper describes an enhancement to co-visibility local map building by incorporating a strong appearance prior, which leads to a more compact local map and latency reduction in downstream data association.
no code implementations • 19 May 2019 • Yipu Zhao, Patricio A. Vela
This paper aims to select features that contribute most to the pose estimation in VO/VSLAM.
Robotics
no code implementations • ECCV 2018 • Yipu Zhao, Patricio A. Vela
This paper tackles a problem in line-assisted VO/VSLAM: accurately solving the least squares pose optimization with unreliable 3D line input.
4 code implementations • 1 Feb 2018 • Fu-Jen Chu, Ruinian Xu, Patricio A. Vela
By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot.
Robotics
no code implementations • 15 Jan 2016 • Guangcong Zhang, Mason J. Lilly, Patricio A. Vela
A codeword for bag-of-words models is generated by packaging the learned descriptor and mask, with a masked Hamming distance defined to measure the distance between two codewords.
no code implementations • 26 Jul 2015 • Hassan A. Kingravi, Patricio A. Vela, Alexandar Gray
This paper presents a practical, and theoretically well-founded, approach to improve the speed of kernel manifold learning algorithms relying on spectral decomposition.
no code implementations • CVPR 2015 • Guangcong Zhang, Patricio A. Vela
Efficient computation strategies for the observability indices are described based on incremental singular value decomposition (SVD) and greedy selection for the temporal and instantaneous observability indices, respectively.
1 code implementation • 10 Sep 2012 • M. Emre Celebi, Hassan A. Kingravi, Patricio A. Vela
K-means is undoubtedly the most widely used partitional clustering algorithm.