Marek Kraft
Papers
11
Total Citations
222
H-Index
8
About
Marek Kraft is a leading researcher in robotics and computer vision, with a primary focus on visual navigation, autonomous systems, and deep learning for robotics. His major contributions lie in the systematic evaluation and benchmarking of image feature detectors and descriptors for robot navigation, as demonstrated by his highly cited works such as "An Evaluation of Image Feature Detectors and Descriptors for Robot Navigation" (53 citations) and "Comparative assessment of point feature detectors in the context of robot navigation" (37 citations). These studies have provided foundational insights for the robotics community, guiding the selection of robust visual features for SLAM and localization. Kraft has also advanced practical applications, notably developing a fast, lightweight deep learning vision pipeline for autonomous UAV landing (27 citations, 2024), addressing critical challenges in aerial robotics. His work on RGB-D data processing and multi-camera calibration for visual navigation benchmarking (22 citations) has enabled more accurate evaluation of navigation algorithms. More recently, he has ventured into space robotics with LunarSim, a high-fidelity lunar rover simulator for ROS 2 (13 citations, 2023). With over 220 total citations across his top works, Kraft’s research continues to shape both terrestrial and extraterrestrial autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6An Indoor RGB-D Dataset for the Evaluation of Robot Navigation Algorithms20 citations · 2013
- 7
- 8
- 9
- 10Hardware implementation of ground classification for a walking robot4 citations · 2013