RESEARCH PAPERYear 2017

Factor Graphs for Robot Perception

Frank Dellaert; Michael Kaess

Classification

Major category: Foundational work
Quadrant: Not applicable
Architecture: Not applicable
Prediction paradigm: Not applicable
Subcategories: 3D representations & state estimation Theory & planning
Source review status: Explicit in survey

Category review. Factor Graphs for Robot Perception gives pre-2026 general probabilistic state-estimation and SLAM machinery. The inspected derivations support a mathematical perception/state foundation, not a task-specific world-action model. Reading evidence

AT A GLANCE

Contribution

A contribution summary has not been added yet.

Abstract

An abstract has not been added yet.

Affiliations

Not listed in the collection.

BibTeX

@article{Dellaert2017FactorGraphs,
  author       = {Frank Dellaert and
                  Michael Kaess},
  title        = {Factor Graphs for Robot Perception},
  journal      = {Found. Trends Robot.},
  volume       = {6},
  number       = {1-2},
  pages        = {1--139},
  year         = {2017},

}

@article{Dellaert_2017,
  title     = {Factor Graphs for Robot Perception},
  volume    = {6},
  issn      = {1935-8261},
  journal   = {Foundations and Trends in Robotics},
  publisher = {Emerald},
  author    = {Dellaert, Frank and Kaess, Michael},
  year      = {2017},
  pages     = {1–139}
}

Classification

Contribution

Abstract

Affiliations

BibTeX

More in this category

Critique of World Model

Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Mean Flows for One-step Generative Modeling