Mark Campbell

About

Mark Campbell joined the faculty of the Sibley School of Mechanical and Aerospace Engineering at Cornell University in 2001 and holds the position of John A. Mellowes ’60 Professor of Mechanical and Aerospace Engineering. His research broadly impacts aerospace and robotic systems, focusing on control and autonomy for robotics, aircraft, and spacecraft. His areas of expertise include machine learning, perception and sensor fusion, optimization and learning-based control and planning, decentralized and distributed estimation and control across teams, and human-robotic interaction. Campbell's work encompasses control of flexible structures, formation flying spacecraft, student-designed satellites, cooperative UAVs, self-driving cars, and human-robotic teaming.

Research topics

• Computer Science
• Artificial Intelligence
• Geography
• Remote sensing
• Computer vision

Selected publications

• End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 202 citations

  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 are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for…

  DOI

• Train in Germany, Test in the USA: Making 3D Object Detectors Generalize

  2020-06-01 · 165 citations

  article

  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 at generalization, they are also notorious to overfit to all kinds of spurious artifacts, such as brightness, car sizes and models, that may appear consistently throughout the data. In fact, most datasets for autonomous driving are collected within a narrow subset of cities within one country, typically under similar…

  PublisherDOI

• Wasserstein Distances for Stereo Disparity Estimation

  arXiv (Cornell University) · 2020-07-06 · 46 citations

  preprintOpen access

  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 disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes further problems in ambiguous regions around object boundaries. We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values, and…

  PublisherOA PDFDOI

• Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs

  2023-05-29 · 11 citations

  articleSenior author

  A self-driving car must be able to reliably handle adverse weather conditions (e.g., snowy) to operate safely. In this paper, we investigate the idea of turning sensor inputs (i.e., images) captured in an adverse condition into a benign one (i.e., sunny), upon which the downstream tasks (e.g., semantic segmentation) can attain high accuracy. Prior work primarily formulates this as an unpaired image-to-image translation problem due to the lack of paired images captured under the exact same camera…

  PublisherDOI

• Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization

  2023-05-29 · 6 citations

  articleSenior author

  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 accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual localization for autonomous driving, where we predict locations from images. Our proposed framework estimates probabilistic uncertainty by cre…

  PublisherDOI

Recent grants

• NRI: FND: Probabilistic Hypothesis-Driven Adaptive Human-Robot Teams

  NSF · $664k · 2018–2023

• EHS: Hybrid Estimation and Control with Bounded Probabilities

  NSF · $270k · 2004–2007

• S&AS: INT: Inference, Reasoning, and Learning for Robust Autonomous Driving

  NSF · $1.4M · 2017–2023

Frequent coauthors

• Giuseppe C. Calafiore

  Polytechnic University of Turin

  65 shared

• Karen Rudie

  Queen's University

  64 shared

• Wei‐Lun Chao

  40 shared

• Kilian Q. Weinberger

  36 shared

• Bharath Hariharan

  32 shared

• Nisar Ahmed

  29 shared

• Daniel E. Clark

  University of Southampton

  25 shared

• Yurong You

  23 shared

Awards & honors

• U.S. Air Force Chief of Staff Award for Exceptional Public S…
• Cornell Stephen H. Weiss Presidential Fellow Award
• Ralph S. Watts `72 Award
• Douglas Whitney Award
• Stephen Miles `57 Award

Similar researchers at Cornell University

• Fengqi Youh-83
• Stephen B. Popeh-83
• Rebecca J. Barthelmieh-62