Papers
6
Total Citations
39
H-Index
4
About
Abhinav Rajvanshi is a roboticist whose research lies at the intersection of autonomous navigation, sensor fusion, and semantic scene understanding. His work addresses the fundamental challenge of enabling robots to operate reliably in visually-degraded environments—such as dark tunnels, smoke-filled rooms, or cluttered industrial sites—where conventional sensors fail. Rajvanshi’s most-cited paper, “Multi-Sensor Fusion for Motion Estimation in Visually-Degraded Environments” (2019, 10 citations), demonstrates how low-cost sensors can be combined to achieve accurate motion estimation for ground robots in infrastructure inspection and indoor rescue missions. He further advanced the field with “SIGNAV” (2022, 7 citations), a semantically-informed GPS-denied navigation and mapping system that leverages scene understanding to maintain performance where vision-based SLAM degrades. His work on “Graph2Nav” (2025) introduces a novel framework for generating 3D object-relation graphs to guide real-time autonomous navigation. Beyond navigation, Rajvanshi contributed to the PROTONPACK system (2017, 6 citations), a handheld visuo-haptic recorder for surface interaction analysis. With a cumulative impact spanning sensor fusion, path planning, and semantic mapping, his research is shaping the next generation of resilient, perceptive autonomous systems for challenging real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Multi-Sensor Fusion for Motion Estimation in Visually-Degraded Environments10 citations · 2019
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- 6Graph2Nav: 3D Object-Relation Graph Generation to Robot Navigation2 citations · 2025