no code implementations • 19 Mar 2024 • Shuo Sun, Malcolm Mielle, Achim J. Lilienthal, Martin Magnusson
We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction.
2 code implementations • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 • Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, Henrik Andreasson
This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation.
Ranked #1 on Radar odometry on Oxford Radar RobotCar Dataset
no code implementations • 17 Feb 2021 • Andrey Rudenko, Luigi Palmieri, Johannes Doellinger, Achim J. Lilienthal, Kai O. Arras
Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance.
Autonomous Driving Robotics
1 code implementation • 19 Apr 2020 • Tomasz Piotr Kucner, Stephanie Lowry, Martin Magnusson, Achim J. Lilienthal
Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e. g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.
Robotics Functional Analysis
no code implementations • 26 Apr 2019 • Achim J. Lilienthal, Maike Schindler
Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER).
1 code implementation • 28 Sep 2017 • Malcolm Mielle, Martin Magnusson, Achim J. Lilienthal
We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.
Robotics
1 code implementation • 16 Feb 2017 • Malcolm Mielle, Martin Magnusson, Henrik Andreasson, Achim J. Lilienthal
Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result.
Robotics