Scott Lenser
Papers
13
Total Citations
456
H-Index
8
About
Scott Lenser is a leading researcher in robotics, with key contributions spanning localization, behavior-based architectures, and lifelong mapping. His most influential work, "Sensor resetting localization for poorly modelled mobile robots" (286 citations), introduced a robust extension of Monte Carlo localization that uses sensor-based re-sampling to recover robot position when lost, even with poor environmental models—a critical advance for real-world deployment. Lenser also pioneered modular hierarchical behavior-based architectures, enabling complex, adaptive robot control, and co-developed CMRoboBits, an influential course at Carnegie Mellon University that taught students to build intelligent AIBO robots, integrating perception, cognition, and action. His research on vision-servoed localization and behavior-based planning for quadruped robots, as well as automatic detection of environmental change, has advanced robot autonomy in dynamic settings. Notably, his recent work on lifelong mapping, ensuring map stability and accuracy over time across thousands of robots, demonstrates ongoing impact in scalable, real-world robotics. With over 400 total citations, Lenser’s contributions are foundational to robust localization and adaptive robot behavior.
Research Focus
Key Achievements
Top Papers
- 1Sensor resetting localization for poorly modelled mobile robots286 citations · 2002
- 2A Modular Hierarchical Behavior-Based Architecture26 citations · 2002
- 3CMRoboBits: Creating an Intelligent AIBO Robot26 citations · 2006
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- 6Automatic detection and response to environmental change19 citations · 2004
- 7Non-Parametric Time Series Classification14 citations · 2006
- 8Fast Parametric Transitions for Smooth Quadrupedal Motion14 citations · 2002
- 9On-line robot adaptation to environmental change7 citations · 2005
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