Maximilian Beinhofer

About

Maximilian Beinhofer is a robotics researcher whose work spans mobile robot navigation, probabilistic mapping, and autonomous service robotics. His most influential contribution, "Occupancy Grid Models for Robot Mapping in Changing Environments" (2021, 124 citations), addresses a critical gap in robotics by developing frameworks that account for dynamic, real-world environments rather than the static assumptions underlying most prior mapping approaches. This work has become a key reference in the field of robot mapping and trajectory planning. A significant thread throughout Beinhofer's career is the strategic deployment of artificial landmarks to enhance robot localization. Through a series of papers from 2011 to 2013 — collectively accumulating nearly 130 citations — he developed near-optimal and robust methods for landmark selection and placement, tackling the fundamental challenge of navigating ambiguous or featureless environments. These contributions have practical relevance for commercial and service robotics applications. Beinhofer has also made notable strides in robotic cleaning, proposing probabilistic guarantees for low-cost cleaning robots and Poisson-driven dirt modeling for efficient coverage paths. More recently, his 2023 benchmarking dataset for robotic bin packing signals an expanding research agenda into logistics and manipulation. Across his career, Beinhofer's work reflects a consistent commitment to bridging theoretical probabilistic methods with practical autonomous systems challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   Occupancy Grid Models for Robot Mapping in Changing Environments

   124 citations · 2021
2. 2
   Effective landmark placement for accurate and reliable mobile robot navigation

   39 citations · 2012
3. 3
   A probabilistic approach to high-confidence cleaning guarantees for low-cost cleaning robots

   31 citations · 2014
4. 4
   Using artificial landmarks to reduce the ambiguity in the environment of a mobile robot

   31 citations · 2011
5. 5
   Near-optimal landmark selection for mobile robot navigation

   19 citations · 2011
6. 6
   Robust landmark selection for mobile robot navigation

   18 citations · 2013
7. 7
   Poisson-driven dirt maps for efficient robot cleaning

   18 citations · 2013
8. 8
   Deploying artificial landmarks to foster data association in simultaneous localization and mapping

   14 citations · 2013
9. 9
   BED-BPP: Benchmarking dataset for robotic bin packing problems

   9 citations · 2023
10. 10
    Landmark Placement for Accurate Mobile Robot Navigation.

    4 citations · 2011

Key Collaborators

Contact & Links