Matthias Faessler

Computer Vision and Pattern Recognition · 2015–2017

A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 49 citations into it; #7,588 of 21,575 by trust here, #5,422 by citations
• Aerospace Engineering — 45 citations into it; #1,120 of 6,421 by trust here, #773 by citations
• Control and Systems Engineering — 25 citations into it; #5,787 of 8,257 by trust here, #5,490 by citations
• Artificial Intelligence — 13 citations into it
• Robotics & Control — 6 citations into it; #251 of 609 by trust here, #154 by citations
• Biomedical Engineering — 5 citations into it

Whom Matthias Faessler trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Daniel Mellinger
2. Vijay R. Kumar
3. Davide Scaramuzza
4. Christian Forster
5. Abraham Bachrach
6. Matia Pizzoli
7. Lorenz Meier
8. John A. Nelder
9. Samir Bouabdallah
10. James Svacha

Who trusts Matthias Faessler overall

1. Davide Falanga
2. Antonio Loquercio
3. Davide Scaramuzza
4. Jeffrey A. Delmerico
5. Guoquan Huang
6. Elia Kaufmann
7. Zhepei Wang
8. Mattia Segu
9. Alessio Zanchettin
10. Nikolai Smolyanskiy

Papers from Matthias Faessler's perspective

Ordered by how strongly the researchers Matthias Faessler trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Minimum snap trajectory generation and control for quadrotors
3. The GRASP Multiple Micro-UAV Testbed (IEEE robotics & automation magazine)
4. Autonomous flight in unstructured and unknown indoor environments
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. A simple learning strategy for high-speed quadrocopter multi-flips (IEEE International Conference on Robotics and Automation)
8. Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor
9. Gradient-based learning applied to document recognition
10. Pattern Recognition and Machine Learning

Their papers

• A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots (2016) — 688 citations
• Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle (2016) — 241 citations
• Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision (2016) — 196 citations
• Automatic re-initialization and failure recovery for aggressive flight with a monocular vision-based quadrotor (2015) — 111 citations
• Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories (2017) — 403 citations
• Thrust Mixing, Saturation, and Body-Rate Control for Accurate Aggressive Quadrotor Flight (2017) — 137 citations