David G. Mohler
Papers
1
Total Citations
2
H-Index
1
About
David G. Mohler is a researcher whose work centers on probabilistic robotics and state estimation, with a particular focus on the practical challenges of deploying particle filtering algorithms in real-world robotic systems. His most cited study, "A study of particle filtering approaches for the kidnapped robot problem" (2018), tackles one of robotics' most vexing localization failures—the "kidnapped robot problem," where a robot is suddenly displaced without knowledge of its new location. Mohler's contribution lies in critically examining the computational feasibility of particle filters under such extreme uncertainty, highlighting the tension between algorithmic robustness and real-time performance. While his citation count remains modest, his work addresses a fundamental bottleneck in autonomous navigation: ensuring that theoretically sound estimation methods remain computationally tractable when faced with multi-modal, non-linear problems. This research is particularly valuable for students and engineers developing robots that must recover from localization failures without human intervention. Mohler's findings serve as a practical cautionary note, reminding the field that algorithmic elegance must be balanced against the hard constraints of onboard processing power and time-critical decision-making.
Research Focus
Key Achievements
Top Papers
- 1A study of particle filtering approaches for the kidnapped robot problem2 citations · 2018