5 citations · 21 across the 20 of their papers we have counts for
6 papers · 1 filter
A Semantic and Occlusion-Aware GM-PHD Filter
Jovan Menezes, Mark Campbell
This paper proposes a new birth model including semantic information derived from deep learning to create an occlusion-aware Gaussian Mixture Probability Hypothesis Density (GM-PHD…
Multiple-Hypothesis Path Planning with Uncertain Object Detections
Brian H. Wang, Beatriz Asfora, Rachel Zheng +3
Path planning in obstacle-dense environments is a key challenge in robotics, and depends on inferring scene attributes and associated uncertainties. We present a multiple-hypothesi…
Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization
Junan Chen, Josephine Monica, Wei-Lun Chao +1
The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making acc…
Planning Paths through Occlusions in Urban Environments
Yutao Han, Youya Xia, Guo-Jun Qi +1
This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navi…
Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments
Vikram Shree, Sarah Allen, Beatriz Asfora +2
There has been a plethora of work towards improving robot perception and navigation, yet their application in hazardous environments, like during a fire or an earthquake, is still…
Is it Worth to Reason about Uncertainty in Occupancy Grid Maps during Path Planning?
Jacopo Banfi, Lindsey Woo, Mark Campbell
This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy…