Kristiaan Schreve
Papers
5
Total Citations
12
H-Index
2
About
Kristiaan Schreve is a robotics researcher whose work centers on the intersection of autonomous navigation, 3D reconstruction, and human-robot interaction. His primary contributions lie in advancing **Visual Simultaneous Localization and Mapping (SLAM)** —a cornerstone of modern robotics—by tackling the persistent challenges of dynamic environments and real-world scalability. Schreve’s research addresses the critical gap between theoretical SLAM performance and practical deployment, particularly for tasks like bin-picking and collaborative robotics. His 2018 paper, “Follow Me: Real-Time in the Wild Person Tracking Application for Autonomous Robotics,” (5 citations) demonstrates a practical application of person-following robots. He has also developed a closed-form solution for scaling monocular LSD-SLAM point clouds (2017, 3 citations) and a methodology for analyzing the accuracy of 3D objects reconstructed via collaborative robots (2018, 2 citations). More recently, his 2024 work on safety-class semantic costmaps introduces risk-aware navigation by integrating semantic information from RGBD sensors, while his research on dynamic human-object interaction detection aims to make SLAM robust in crowded, real-world settings. Schreve’s work is steadily gaining traction, reflecting the growing demand for robots that can perceive, navigate, and interact safely in human-centric environments.
Research Focus
Key Achievements
Top Papers
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