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About

Kirill Muravyev is an emerging robotics researcher whose work sits at the intersection of mobile robot navigation, mapping, and autonomous systems. His research focuses primarily on topological mapping, simultaneous localization and mapping (SLAM), and path planning for robots operating across diverse and challenging environments. Muravyev has made notable contributions to topological mapping methodologies, most prominently through his development of PRISM-TopoMap, an online topological mapping system that integrates place recognition and scan matching to support robust long-term autonomous navigation. His comparative evaluation of topological mapping methods (11 citations) provides the robotics community with a valuable benchmark resource, while his work on 2.5D navigation for uneven terrain (10 citations) demonstrates practical solutions combining elevation mapping with Model Predictive Path Integral control. His exploration of RGB-D SLAM in large indoor environments further strengthens the field's understanding of scalable mapping techniques. Beyond mapping, Muravyev has contributed to Embodied AI through skill-fusion approaches for visual object-goal navigation and developed open-source ROS-compatible tools for monocular depth reconstruction, lowering barriers for researchers integrating deep learning into robotic pipelines. With a growing citation record accumulating over 65 citations across a focused body of work, Muravyev represents a promising voice in autonomous mobile robotics research.