Krishna Kanakgiri
Papers
1
Total Citations
3
H-Index
1
About
Krishna Kanakgiri is a researcher focused on advancing autonomous vehicle navigation through sensor fusion and real-time control systems. His work centers on integrating cost-effective perception technologies—such as single RGB cameras and 2D LIDAR—with path-planning algorithms for Ackermann steering robots, addressing critical challenges in self-driving car development. His most-cited paper, "Real-time localisation and path-planning in ackermann steering robot using a single RGB camera and 2D LIDAR" (2017), proposes a lightweight, rapid-prototyping approach that reduces reliance on expensive sensor suites while maintaining localization accuracy. This contribution is particularly significant for democratizing autonomous vehicle research, enabling smaller labs and startups to test navigation algorithms on accessible platforms. Though his citation count is modest, Kanakgiri’s work directly tackles the dual problems of road accidents and traffic pollution by promoting efficient, scalable autonomy. His research aligns with broader efforts to bridge the gap between simulation and real-world deployment, offering practical insights for students and engineers building low-cost autonomous prototypes.
Research Focus
Key Achievements
Top Papers
- 1