Denis Shepelev
Papers
1
Total Citations
6
H-Index
1
About
Denis Shepelev is a researcher in autonomous robotics, with a primary focus on sensor-based environmental mapping and localization. His work centers on developing efficient algorithms for occupancy grid mapping, a critical function for robot navigation and path planning. Shepelev’s most cited paper, “Occupancy grid mapping with the use of a forward sonar model by gradient descent” (2016), introduces an innovative approach that leverages gradient descent optimization to improve mapping accuracy from low-cost sonar sensors. This contribution is notable for enabling robust spatial representation using affordable, easy-to-install hardware, making autonomous navigation more accessible. With 6 citations, this work has informed subsequent research in sensor fusion and probabilistic mapping. Shepelev’s research addresses the practical challenge of balancing sensor cost with mapping fidelity, a key concern in field robotics. His achievements highlight a commitment to advancing real-world autonomous systems through computationally efficient, sensor-driven methodologies.
Research Focus
Key Achievements
Top Papers
- 1