World's Best Scientists 2026 revealed!
Cesar Cadena

Cesar Cadena

D-Index & Metrics

Computer Science

D-Index
41
Citations
14786
World Ranking
8589
National Ranking
159

Overview

Cesar Cadena is affiliated with ETH Zurich in Switzerland. Their research focuses on several intersecting fields within computer science and engineering, with a strong emphasis on robotics and sensor-based localization.

The main fields of study that characterize Cesar Cadena's work are:

  • Computer Science
  • Engineering

The scientist's research spans a set of specialized subfields, including:

  • Computer Vision and Pattern Recognition
  • Aerospace Engineering
  • Artificial Intelligence
  • Control and Systems Engineering
  • Environmental Engineering

Cadena's work topics cover a range of themes related to robotics, machine learning, and sensory data analysis. These include:

  • Robotics and Sensor-Based Localization
  • Advanced Neural Network Applications
  • Advanced Vision and Imaging
  • Advanced Image and Video Retrieval Techniques
  • Robotic Path Planning Algorithms
  • Multimodal Machine Learning Applications
  • Remote Sensing and LiDAR Applications

Frequent collaborators listed in Cadena's research include Roland Siegwart, Hermann Blum, Marco Hutter, Abel Gawel, and René Zurbrügg. These co-authors reflect a network of partnerships in robotics and automation research.

Cadena has published extensively, with significant contributions in various venues. The most common publication venues for their research are:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • Zenodo (CERN European Organization for Nuclear Research)
  • 2022 International Conference on Robotics and Automation (ICRA)
  • IEEE Transactions on Robotics

Some recent notable papers authored or co-authored by Cesar Cadena include:

  • The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation, 2021, International Journal of Computer Vision
  • Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • maplab 2.0 - A Modular and Multi-Modal Mapping Framework, 2022, IEEE Robotics and Automation Letters
  • Empty Cities: A Dynamic-Object-Invariant Space for Visual SLAM, 2020, IEEE Transactions on Robotics
  • A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments Under Severe Odometry Drift, 2021, IEEE Robotics and Automation Letters

Best Publications

  • Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

    Cesar Cadena;Luca Carlone;Henry Carrillo;Yasir Latif

  • Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

    Cesar Cadena;Luca Carlone;Henry Carrillo;Yasir Latif

  • From Coarse to Fine: Robust Hierarchical Localization at Large Scale

    Paul-Edouard Sarlin;Cesar Cadena;Roland Siegwart;Marcin Dymczyk

  • From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots

    Mark Pfeiffer;Michael Schaeuble;Juan Nieto;Roland Siegwart

  • Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

    Margarita Grinvald;Fadri Furrer;Tonci Novkovic;Jen Jen Chung

  • The current state and future outlook of rescue robotics

    Jeffrey A. Delmerico;Stefano Mintchev;Alessandro Giusti;Boris Gromov

  • SegMatch: Segment based place recognition in 3D point clouds

    Renaud Dube;Daniel Dugas;Elena Stumm;Juan Nieto

  • Robust loop closing over time for pose graph SLAM

    Yasir Latif;César Cadena;José Neira

  • SegMatch: Segment based loop-closure for 3D point clouds.

    Renaud Dubé;Daniel Dugas;Elena Stumm;Juan I. Nieto

  • SegMap: Segment-based mapping and localization using data-driven descriptors:

    Renaud Dubé;Andrei Cramariuc;Daniel Dugas;Hannes Sommer

  • Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

    Mark Pfeiffer;Samarth Shukla;Matteo Turchetta;Cesar Cadena

  • SegMap: 3D Segment Mapping using Data-Driven Descriptors

    Renaud Dubé;Andrei Cramariuc;Daniel Dugas;Juan I. Nieto

  • Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning

    Lorenz Wellhausen;Alexey Dosovitskiy;Rene Ranftl;Krzysztof Walas

  • X-View: Graph-Based Semantic Multi-View Localization

    Abel Gawel;Carlo Del Don;Roland Siegwart;Juan I. Nieto

  • Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps

    Victor Reijgwart;Alexander Millane;Helen Oleynikova;Roland Siegwart

  • An online multi-robot SLAM system for 3D LiDARs

    Renaud Dube;Abel Gawel;Hannes Sommer;Juan Nieto

  • The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

    Hermann Blum;Paul-Edouard Sarlin;Juan I. Nieto;Roland Siegwart

  • Pixel-wise Anomaly Detection in Complex Driving Scenes

    Giancarlo Di Biase;Hermann Blum;Roland Siegwart;Cesar Cadena

  • A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

    Mark Pfeiffer;Giuseppe Paolo;Hannes Sommer;Juan Nieto

  • Real-time 6-DOF multi-session visual SLAM over large-scale environments

    J. Mcdonald;M. Kaess;C. Cadena;J. Neira

  • Robust Place Recognition With Stereo Sequences

    C. Cadena;D. Galvez-López;J. D. Tardos;J. Neira

  • Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding

    Cesar Cadena;Anthony R. Dick;Ian D. Reid

Frequent Co-Authors

Juan Nieto
Juan Nieto Microsoft (United States)
José Neira
José Neira University of Zaragoza
Ian Reid
Ian Reid University of Adelaide
Davide Scaramuzza
Davide Scaramuzza University of Zurich
Jana Kosecka
Jana Kosecka George Mason University
Ben Upcroft
Ben Upcroft Queensland University of Technology
Marco Hutter
Marco Hutter ETH Zurich
Federico Tombari
Federico Tombari Technical University of Munich

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education can open doors to a variety of tech-driven careers beyond computer science. Many students consider a bachelor of science in physics online as a pathway to deepen analytical and problem-solving skills. This foundational knowledge is valuable in industries such as engineering, data analysis, and tech development.

For those focused on data, pursuing an affordable data science degree can be a smart move. Data science is a rapidly growing field, and online programs make it accessible to more students while minimizing costs.

Engineering remains a popular and lucrative tech career. Attending one of the top online electrical engineering schools enables flexibility, allowing learners to balance work and study. This can lead to specialized jobs in electronics, power systems, and robotics.

If you’re looking for faster entry into the workforce, consider seeking easy certifications that pay well. These programs focus on high-demand skills and can offer a quicker, more direct route to well-paid technical positions.

Best Scientists Citing Cesar Cadena

Trending Scientists

Recently Published Articles