Papers
15
Total Citations
103
H-Index
6
About
Marcus Strand is a robotics researcher whose work spans autonomous robot navigation, 3D environment modeling, and industrial robot programming. His early contributions in the mid-2000s helped advance the field of autonomous mobile robotics, particularly through the development of sophisticated navigation architectures for dynamic, human-centered environments. His 2008 next-best-view planning work, which earned 21 citations, demonstrated how attributed 2D-grids could guide autonomous robots in constructing accurate 3D models using laser scanners — a significant step toward self-directed robotic perception. Complementing this, his research on range image registration using octree-based strategies addressed a fundamental challenge in consistent world modeling for autonomous systems. Strand's work also extended into object recognition and manipulation, with notable contributions on superquadric-based point cloud segmentation for robotic grasping tasks. More recently, his research has shifted toward industrial applications, including the acclaimed RoboGrind system (2024), which enables intuitive, interactive automation of surface treatment tasks such as grinding and polishing — areas notoriously resistant to automation. His contributions to the *Intelligent Autonomous Systems* conference proceedings further underscore his sustained engagement with the broader robotics research community. With a body of work spanning over two decades, Strand has made enduring contributions to both foundational and applied robotics research.
Research Focus
Key Achievements
Top Papers
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- 3Range Image Registration Using an Octree based Matching Strategy13 citations · 2007
- 4Intelligent Autonomous Systems 159 citations · 2018
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- 6Intelligent Autonomous Systems 187 citations · 2024
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- 10Predictive robot programming3 citations · 2003