World's Best Scientists 2026 revealed!

Overview

Michael Kaess is affiliated with Carnegie Mellon University in the United States, with a research focus centered around engineering and computer science. Their scholarly work spans multiple subfields including computer vision and pattern recognition, aerospace engineering, artificial intelligence, ocean engineering, and electrical and electronic engineering.

The scientist has contributed extensively to topics related to robotics and sensor-based localization, advanced vision and imaging, underwater vehicles and communication systems, indoor and outdoor localization technologies, tactile and sensory interactions, advanced image and video retrieval techniques, and image and object detection techniques.

Michael Kaess has a significant record of publications in various scientific venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2022 International Conference on Robotics and Automation (ICRA)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Recent papers authored or coauthored by Michael Kaess include:

  • NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation, 2024, Science Robotics
  • HoloOcean: An Underwater Robotics Simulator, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • EDPLVO: Efficient Direct Point-Line Visual Odometry, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • LiDAR SLAM With Plane Adjustment for Indoor Environment, 2021, IEEE Robotics and Automation Letters
  • DPLVO: Direct Point-Line Monocular Visual Odometry, 2021, IEEE Robotics and Automation Letters

The scientist frequently collaborates with a number of researchers, including:

  • Lipu Zhou
  • Joshua G. Mangelson
  • Paloma Sodhi
  • Easton Potokar
  • Mohamad Qadri

Best Publications

  • iSAM2: Incremental smoothing and mapping using the Bayes tree

    Michael Kaess;Hordur Johannsson;Richard Roberts;Viorela Ila

  • iSAM: Incremental Smoothing and Mapping

    M. Kaess;A. Ranganathan;F. Dellaert

  • Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

    Frank Dellaert;Michael Kaess

  • Kintinuous: Spatially Extended KinectFusion

    Thomas Whelan;Michael Kaess;Maurice Fallon;Hordur Johannsson

  • Adolescent subthreshold-depression and anxiety: psychopathology, functional impairment and increased suicide risk

    Judit Balázs;Mónika Miklósi;Ágnes Keresztény;Christina W. Hoven

  • Prevalence of pathological internet use among adolescents in Europe: demographic and social factors

    Tony Durkee;Michael Kaess;Vladimir Carli;Peter Parzer

  • Real-time large-scale dense RGB-D SLAM with volumetric fusion

    Thomas Whelan;Michael Kaess;Hordur Johannsson;Maurice Fallon

  • Robust real-time visual odometry for dense RGB-D mapping

    Thomas Whelan;Hordur Johannsson;Michael Kaess;John J. Leonard

  • Factor Graphs for Robot Perception

    Frank Dellaert;Michael Kaess

  • Advanced perception, navigation and planning for autonomous in-water ship hull inspection

    Franz S. Hover;Ryan M. Eustice;Ayoung Kim;Brendan J. Englot

  • Information fusion in navigation systems via factor graph based incremental smoothing

    Vadim Indelman;Stephen Williams;Michael Kaess;Frank Dellaert

  • iSAM2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering

    Michael Kaess;Hordur Johannsson;Richard Roberts;Viorela Ila

  • Multiple relative pose graphs for robust cooperative mapping

    Been Kim;Michael Kaess;Luke Fletcher;John Leonard

  • Imaging sonar-aided navigation for autonomous underwater harbor surveillance

    Hordur Johannsson;Michael Kaess;Brendan Englot;Franz Hover

  • Automatic Extrinsic Calibration of a Camera and a 3D LiDAR Using Line and Plane Correspondences

    Lipu Zhou;Zimo Li;Michael Kaess

  • Covariance recovery from a square root information matrix for data association

    Michael Kaess;Frank Dellaert

  • Simultaneous localization and mapping with infinite planes

    Michael Kaess

  • On degeneracy of optimization-based state estimation problems

    Ji Zhang;Michael Kaess;Sanjiv Singh

  • Real-time depth enhanced monocular odometry

    Ji Zhang;Michael Kaess;Sanjiv Singh

  • Incremental smoothing and mapping

    Frank Dellaert;Michael Kaess

Frequent Co-Authors

Frank Dellaert
Frank Dellaert Georgia Institute of Technology
Guoquan Huang
Guoquan Huang University of Delaware
Sanjiv Singh
Sanjiv Singh Carnegie Mellon University
Sebastian Scherer
Sebastian Scherer Carnegie Mellon University
Ronald C. Arkin
Ronald C. Arkin Georgia Institute of Technology
Ryan M. Eustice
Ryan M. Eustice University of Michigan–Ann Arbor
Maxim Likhachev
Maxim Likhachev Carnegie Mellon University
Siddhartha S. Srinivasa
Siddhartha S. Srinivasa University of Washington
Chelsea Finn
Chelsea Finn Stanford University

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