World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
13588
World Ranking
2791
National Ranking
1380

Juan Nieto 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 Juan Nieto 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: 228 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Juan Nieto 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 Juan Nieto 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: 63 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Juan Nieto is a researcher affiliated with Microsoft in the United States, with a primary focus on fields such as Engineering and Computer Science. Their body of work encompasses various subfields including Computer Vision and Pattern Recognition, Aerospace Engineering, Control and Systems Engineering, Artificial Intelligence, and Biomedical Engineering.

Their research topics cover a broad spectrum related to robotics, sensor technologies, and machine learning, with an emphasis on areas such as:

  • Robotics and Sensor-Based Localization
  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • UAV Applications and Optimization
  • Advanced Image and Video Retrieval Techniques
  • Advanced Neural Network Applications
  • Indoor and Outdoor Localization Technologies

Juan Nieto has contributed to numerous publications in notable scientific venues. Frequent publication outlets include:

  • IEEE Robotics and Automation Letters
  • arXiv (Cornell University)
  • IEEE Robotics & Automation Magazine
  • Journal of Field Robotics
  • Repository for Publications and Research Data (ETH Zurich)

Several recent papers illustrate the scope of their research, such as:

  • "An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments" (2020) in IEEE Robotics and Automation Letters
  • "An informative path planning framework for UAV-based terrain monitoring" (2020) in Autonomous Robots
  • "The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation" (2021) in International Journal of Computer Vision
  • "Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution" (2020) in IEEE Robotics & Automation Magazine
  • "CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge" (2022) in Field Robotics

Their collaborative work involves multiple frequent co-authors, including:

  • Roland Siegwart
  • Lionel Ott
  • César Cadena
  • Jen Jen Chung
  • Michael Pantic

Best Publications

  • Consistency of the EKF-SLAM Algorithm

    T. Bailey;J. Nieto;J. Guivant;M. Stevens

  • Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning

    Helen Oleynikova;Zachary Taylor;Marius Fehr;Roland Siegwart

  • 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

  • Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning

    Helen Oleynikova;Zachary Taylor;Marius Fehr;Juan Nieto

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

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

  • weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

    Inkyu Sa;Zetao Chen;Marija Popovic;Raghav Khanna

  • Consistency of the FastSLAM algorithm

    T. Bailey;J. Nieto;E. Nebot

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

    Renaud Dube;Daniel Dugas;Elena Stumm;Juan Nieto

  • WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

    Inkyu Sa;Marija Popovic;Raghav Khanna;Zetao Chen

  • An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

    Lukas Schmid;Michael Pantic;Raghav Khanna;Lionel Ott

  • Approximate Inference in State-Space Models With Heavy-Tailed Noise

    G. Agamennoni;J. I. Nieto;E. M. Nebot

  • 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

  • Real time data association for FastSLAM

    J. Nieto;J. Guivant;E. Nebot;S. Thrun

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

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

  • Continuous-time trajectory optimization for online UAV replanning

    Helen Oleynikova;Michael Burri;Zachary Taylor;Juan Nieto

  • An outlier-robust Kalman filter

    Gabriel Agamennoni;Juan I. Nieto;Eduardo M. Nebot

  • Recursive scan-matching SLAM

    Juan Nieto;Tim Bailey;Eduardo Nebot

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

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

  • FastSLAM: An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data

    Sebastian Thrun;Michael Montemerlo;Daphne Koller;Ben Wegbreit

  • Navigation and Mapping in Large Unstructured Environments

    José E. Guivant;Eduardo Mario Nebot;Juan I. Nieto;Favio R. Masson

  • Robust Inference of Principal Road Paths for Intelligent Transportation Systems

    G Agamennoni;J I Nieto;E M Nebot

Frequent Co-Authors

Cesar Cadena
Cesar Cadena ETH Zurich
Eduardo Nebot
Eduardo Nebot University of Sydney
James Underwood
James Underwood University of Sydney
Salah Sukkarieh
Salah Sukkarieh University of Sydney
Cyrill Stachniss
Cyrill Stachniss University of Bonn
Achim Walter
Achim Walter ETH Zurich
Fabio Ramos
Fabio Ramos University of Sydney
Daniele Nardi
Daniele Nardi Sapienza University of Rome
Paul Beardsley
Paul Beardsley Weta Digital

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