32 citations · 131 across the 10 of their papers we have counts for
8 papers · 1 filter
Planning Paths Through Unknown Space by Imagining What Lies Therein
Yutao Han, Jacopo Banfi, Mark Campbell
This paper presents a novel framework for planning paths in maps containing unknown spaces, such as from occlusions. Our approach takes as input a semantically-annotated point clou…
Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search
Beatriz A. Asfora, Jacopo Banfi, Mark Campbell
In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a movin…
Wasserstein Distances for Stereo Disparity Estimation
Divyansh Garg, Yan Wang, Bharath Hariharan +3
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or dispar…
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
Yan Wang, Xiangyu Chen, Yurong You +5
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike. While deep networks are great…
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Rui Qian, Divyansh Garg, Yan Wang +6
Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they…
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
Yutao Han, Hubert Lin, Jacopo Banfi +2
Planning in unstructured environments is challenging -- it relies on sensing, perception, scene reconstruction, and reasoning about various uncertainties. We propose DeepSemanticHP…