1 citations · 2 across the 12 of their papers we have counts for
12 papers
Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection
Parker Ewen, Dmitriy Rivkin, Mario Bijelic +1
Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking distance requirements at high spee…
ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
Dmitriy Rivkin, Parker Ewen, Lili Gao +5
Recent video diffusion models achieve high-quality generation through recurrent frame processing where each frame generation depends on previous frames. However, this recurrent mec…
SLIM-VDB: A Real-Time 3D Probabilistic Semantic Mapping Framework
Anja Sheppard, Parker Ewen, Joey Wilson +6
This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures fr…
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models
Parker Ewen, Hao Chen, Seth Isaacson +3
This paper introduces a novel approach to uncertainty quantification for radiance fields by leveraging higher-order moments of the rendering equation. Uncertainty quantification is…
Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting
Joey Wilson, Marcelino Almeida, Min Sun +6
In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introd…
LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertainty
Joey Wilson, Ruihan Xu, Yile Sun +4
This paper introduces a novel probabilistic mapping algorithm, LatentBKI, which enables open-vocabulary mapping with quantifiable uncertainty. Traditionally, semantic mapping algor…
Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat
Jonathan Michaux, Seth Isaacson, Challen Enninful Adu +6
Neural Radiance Fields and Gaussian Splatting have recently transformed computer vision by enabling photo-realistic representations of complex scenes. However, they have seen limit…
You've Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction
Parker Ewen, Hao Chen, Yuzhen Chen +4
Robots must be able to understand their surroundings to perform complex tasks in challenging environments and many of these complex tasks require estimates of physical properties s…
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
Joey Wilson, Yuewei Fu, Joshua Friesen +5
In this paper, we develop a modular neural network for real-time {\color{black}(> 10 Hz)} semantic mapping in uncertain environments, which explicitly updates per-voxel probabilist…
Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
Parker Ewen, Gitesh Gunjal, Joey Wilson +3
Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of inter…
These Maps Are Made For Walking: Real-Time Terrain Property Estimation for Mobile Robots
Parker Ewen, Adam Li, Yuxin Chen +2
The equations of motion governing mobile robots are dependent on terrain properties such as the coefficient of friction, and contact model parameters. Estimating these properties i…
Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation
Parker Ewen, Jean-Pierre Sleiman, Yuxin Chen +3
Manipulators can be added to legged robots, allowing them to interact with and change their environment. Legged mobile manipulation planners must consider how contact forces genera…