Aleksandr Vokhmintcev

About

Aleksandr Vokhmintcev is a researcher whose work sits at the intersection of robotics, computer vision, and computational archaeology. His primary contributions lie in developing novel algorithms for mobile robot navigation and mapping, particularly in dynamic and unknown environments. Vokhmintcev has pioneered heuristic path planning methods with theoretically proven computational complexity, and has advanced real-time mapping techniques by fusing Kalman filtering with symbolic tags and rotation-invariant descriptors. His innovative use of the Iterative Close Point (ICP) algorithm for visual loop-closure detection has enabled more robust robot localization. Notably, his most recent work (2024) applies Dynamic Graph CNNs and FICP to the detection and study of archaeological sites, demonstrating the cross-disciplinary impact of his robotics expertise. With his most-cited papers accumulating over 20 citations, Vokhmintcev’s research is recognized for providing practical, computationally efficient solutions to core challenges in autonomous navigation and spatial understanding.

Research Focus

Key Achievements

Top Papers

1. 1
   Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites

   6 citations · 2024
2. 2
   Robot path planning algorithm based on symbolic tags in dynamic environment

   5 citations · 2017
3. 3
   Robot mapping algorithm based on Kalman filtering and symbolic tags

   4 citations · 2017
4. 4
   A Real-Time Algorithm for Mobile Robot Mapping Based on Rotation-Invariant Descriptors and Iterative Close Point Algorithm

   4 citations · 2017
5. 5
   Real-Time visual loop-closure detection using fused iterative close point algorithm and extended Kalman filter

   3 citations · 2017

Key Collaborators

Contact & Links