World's Best Scientists 2026 revealed!
Cesar Cadena

Cesar Cadena

D-Index & Metrics

Computer Science

D-Index
41
Citations
14786
World Ranking
8588
National Ranking
159

Cesar Cadena publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Cesar Cadena sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Cesar Cadena D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Cesar Cadena sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

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