Frank Dellaert

About

Professor Frank Dellaert’s research focuses on large-scale inference for autonomous robot systems, on land, air, and in water. He pioneered the use of several probabilistic methods in both computer vision and robotics. With Dieter Fox and Sebastian Thrun, he introduced the Monte Carlo localization method for estimating and tracking the pose of robots, which is now a standard and popular tool in mobile robotics. More recently, he has investigated 3D reconstruction in large-scale environments by taking a graph-theoretic view and introduced factor graphs into the mainstream language of the robotics community. Professor Dellaert has published more than 200 technical articles, as well as several book chapters. He has received an NSF CAREER award and research grants from NSF, DARPA, ARL, ARO, Intel, Microsoft, Samsung, and others. During a leave from 2014 to 2018, he spent time at his alma mater in Belgium, worked as Chief Scientist at Skydio, and held positions at Facebook’s Reality Labs and Google AI, as well as CTO at Verdant Robotics.

Research topics

• Artificial Intelligence
• Computer Science
• Algorithm
• Mathematical optimization
• Mathematics

Selected publications

• Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022-06-01 · 206 citations

  article

  We present Panoptic Neural Fields (PNF), an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is represented by an oriented 3D bounding box and a multi-layer perceptron (MLP) that takes position, direction, and time and outputs density and radiance. The background stuff is represented by a similar MLP that additionally outputs semantic labels. Each object MLPs are instance-specific and thus can be smaller and faste…

  PublisherDOI

• Factor Graphs: Exploiting Structure in Robotics

  Annual Review of Control Robotics and Autonomous Systems · 2021-01-12 · 88 citations

  articleOpen access1st authorCorresponding

  Many estimation, planning, and optimal control problems in robotics have an optimization problem at their core. In most of these optimization problems, the objective to be maximized or minimized is composed of many different factors or terms that are local in nature—that is, they depend only on a small subset of the variables. A particularly insightful way of modeling this locality structure is to use the concept of factor graphs, a bipartite graphical model in which factors represent functions…

  PublisherOA PDFDOI

• Shonan Rotation Averaging: Global Optimality by Surfing SO(p) <sup>n</sup>

  Lecture notes in computer science · 2021 · 60 citations

  1st authorCorresponding
  DOI

• A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion

  2023-05-29 · 11 citations

  articleSenior author

  We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogram…

  PublisherDOI

• Proprioceptive State Estimation of Legged Robots with Kinematic Chain Modeling

  2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids) · 2022-11-28 · 7 citations

  articleSenior author

  Legged robot locomotion is a challenging task due to a myriad of sub-problems, such as the hybrid dynamics of foot contact and the effects of the desired gait on the terrain. Accurate and efficient state estimation of the floating base and the feet joints can help alleviate much of these issues by providing feedback information to robot controllers. Current state estimation methods are highly reliant on a conjunction of visual and inertial measurements to provide real-time estimates, thus being…

  PublisherDOI

Recent grants

• Unlocking the Urban Photographic Record Through 4D Scene Understanding and Modeling

  NSF · $206k · 2005–2010

• RI: Collaborative Research: Bion-Inspired Navigation

  NSF · $211k · 2007–2011

• CAREER: Markov Chain Monte Carlo Methods for Large Scale Correspondence Problems in Computer Vision and Robotics

  NSF · $422k · 2005–2011

Frequent coauthors

• Luca Carlone

  39 shared

• Michael Kaess

  32 shared

• Vadim Indelman

  23 shared

• Ananth Ranganathan

  20 shared

• Richard Roberts

  17 shared

• Tucker Balch

  16 shared

• Sebastian Thrun

  15 shared

• Gerry Chen

  Georgia Institute of Technology

  15 shared

Education

• Ph.D., Computer Science

  Carnegie Mellon University

  2001

Awards & honors

• NSF CAREER Award (2005)

Similar researchers at Georgia Institute of Technology

• Mark Borodovskyh-64
• Irfan Essah-63
• Mostafa Ammarh-61