Factor Graphs for Robot Perception
By Frank Dellaert, Michael Kaess. Found. Trends Robotics, 2017
721 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/d30c40f372976914f6a433d9bd6c70a2eea54832
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Luca Carlone
- Richard Bamler
- Xiaoxiang Zhu
- Yun Chang
- Daniela Rus
- Carlo Ratti
- Antoni Rosinol
- Nathan Hughes
In Computer Vision and Pattern Recognition
- Luca Carlone
- Daniela Rus
- Carlo Ratti
- Yun Chang
- Antoni Rosinol
- Nathan Hughes
- Brendan Englot
- Tixiao Shan
In Control and Systems Engineering
- Muhammad Shahzad
- Marcus Abate
- Daniela Rus
- Yun Chang
- Luca Carlone
- Nathan Hughes
- Rudolph Triebel
- Arjun Gupta
Citations in the corpus
- 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans (2020, methodology): “Consecutive poses are connected by a factor [20] modeling a zero velocity prior.”
- Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, background): “Consecutive poses are connected by a factor (Dellaert and Kaess, 2017) modeling a zero velocity prior on the human motion with a permissive noise model to allow for small motions.”
- A survey of uncertainty in deep neural networks (2021, methodology): “As a result, many probabilistic methods such as factor graphs [308], [309] are now the work-horse of advanced consumer products such”
- LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “With the assumption of a Gaussian noise model, the MAP inference for our problem is equivalent to solving a nonlinear least-squares problem [18].”