Papers
3
Total Citations
53
H-Index
3
About
Mark Van Loock is a robotics researcher whose work bridges the critical gap between perception and autonomy, focusing on 3D object detection, environmental modeling, and robust visual tracking. His most influential contribution, "Robust on-line model-based object detection from range images" (2009, 47 citations), pioneered a method for mobile robots to classify and localize objects in real-time from 3D laser range data—a foundational step for high-level autonomous tasks. This work remains a key reference in object detection from point clouds. Van Loock further advanced environmental understanding through his research on reflectance mapping, notably in "Building Dense Reflectance Maps of Indoor Environments Using an RGB-D Camera" (2018, 3 citations), which enabled robots to capture material properties beyond simple color. He also tackled the challenge of dynamic illumination in "Real-Time Outdoor Illumination Estimation for Camera Tracking in Indoor Environments" (2021, 3 citations), proposing a method to estimate indoor scene appearance under changing outdoor light—critical for stable robot localization. Together, these contributions demonstrate Van Loock’s impact on creating robots that perceive and adapt to complex, real-world environments, with his early work on online object detection remaining a cornerstone of his legacy.
Research Focus
Key Achievements
Top Papers
- 1Robust on-line model-based object detection from range images47 citations · 2009
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