5 papers
LocoNeRF: A NeRF-based Approach for Local Structure from Motion for Precise Localization
Artem Nenashev, Mikhail Kurenkov, Andrei Potapov +3
Visual localization is a critical task in mobile robotics, and researchers are continuously developing new approaches to enhance its efficiency. In this article, we propose a novel…
DNFOMP: Dynamic Neural Field Optimal Motion Planner for Navigation of Autonomous Robots in Cluttered Environment
Maksim Katerishich, Mikhail Kurenkov, Sausar Karaf +2
Motion planning in dynamically changing environments is one of the most complex challenges in autonomous driving. Safety is a crucial requirement, along with driving comfort and sp…
Hierarchical Visual Localization Based on Sparse Feature Pyramid for Adaptive Reduction of Keypoint Map Size
Andrei Potapov, Mikhail Kurenkov, Pavel Karpyshev +4
Visual localization is a fundamental task for a wide range of applications in the field of robotics. Yet, it is still a complex problem with no universal solution, and the existing…
SwipeBot: DNN-based Autonomous Robot Navigation among Movable Obstacles in Cluttered Environments
Nikolay Zherdev, Mikhail Kurenkov, Kristina Belikova +1
In this paper, we propose a novel approach to wheeled robot navigation through an environment with movable obstacles. A robot exploits knowledge about different obstacle classes an…
CloudVision: DNN-based Visual Localization of Autonomous Robots using Prebuilt LiDAR Point Cloud
Evgeny Yudin, Pavel Karpyshev, Mikhail Kurenkov +4
In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data…