Papers
2
Total Citations
7
H-Index
2
About
Krishnam Gupta’s research lies at the intersection of autonomous navigation, computer vision, and human-robot interaction, with a focus on making mobile systems safer and more perceptive in real-world environments. His work on small obstacle detection using stereo vision addresses a critical gap in autonomous driving: the ability to identify hazards like rocks or bricks that are too small for conventional depth sensors. This contribution, published in 2017, has garnered 4 citations and highlights the practical challenges of deploying autonomous ground vehicles on imperfect roads. In earlier work, Gupta proposed a time-scaled collision cone approach for mobile robot navigation in human-centered spaces, explicitly modeling human intent and uncertainty. This 2015 paper, with 3 citations, introduced a novel framework where the “intent space” is represented as bands of possible human trajectories, enabling non-holonomic robots to anticipate and avoid collisions more gracefully. Though his citation counts are modest, Gupta’s research tackles foundational problems in perception and planning that are essential for the safe integration of autonomous systems into everyday life. His work is particularly valuable for students and researchers interested in bridging the gap between theoretical navigation algorithms and the messy, unpredictable dynamics of human environments.
Research Focus
Key Achievements
Top Papers
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