Marc Arends
Papers
3
Total Citations
63
H-Index
2
About
Marc Arends is a robotics researcher specializing in autonomous navigation and terrain perception for mobile systems operating in unstructured environments. His work focuses on enabling robots to interpret and safely traverse complex, natural terrains through advanced probabilistic modeling and multi-sensor fusion. Arends’ most influential contribution is the development of probabilistic terrain classification methods, as demonstrated in his highly cited 2012 paper (41 citations), which provides a robust framework for categorizing terrain in unpredictable settings. He further advanced this field by integrating camera imagery with 3D laser range data using Markov Random Fields (2011, 20 citations), a technique that models spatial dependencies between neighboring terrain cells to distinguish obstacles from negotiable regions. His research also explores the application of multi-modal features for terrain classification on mobile platforms (2011, 2 citations). Arends’ work has laid critical groundwork for autonomous off-road navigation, directly impacting the design of perception systems for field robots. His contributions remain foundational for researchers developing resilient, terrain-aware autonomous vehicles.
Research Focus
Key Achievements
Top Papers
- 1Probabilistic terrain classification in unstructured environments41 citations · 2012
- 2
- 3