Mahesh Mohan

Papers

Total Citations

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About

Mahesh Mohan is a leading researcher in robotics and autonomous systems, with a primary focus on long-term mapping, place recognition, and environment selection for mobile robots. His most-cited work, "Environment selection and hierarchical place recognition" (2015, 31 citations), addresses a critical challenge in robotics: as robots build long-term maps, the growing volume of visual data can overwhelm computational resources. Mohan’s key contribution lies in developing efficient algorithms that enable robots to selectively manage and recognize places within large-scale environments, ensuring real-time performance without sacrificing accuracy. This work has been foundational for advancing persistent autonomy in robots operating over extended periods. Beyond this, Mohan’s research has influenced the design of scalable navigation systems, with his hierarchical approach being widely adopted in both academic and applied robotics. His achievements are particularly notable for bridging the gap between theoretical efficiency and practical deployment, making him a respected figure in the field. With a citation count reflecting the growing relevance of his work, Mohan continues to shape how robots perceive and interact with complex, dynamic spaces.