4 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
LiePoseNet: Heterogeneous Loss Function Based on Lie Group for Significant Speed-up of PoseNet Training Process
Mikhail Kurenkov, Ivan Kalinov, Dzmitry Tsetserukou
Visual localization is an essential modern technology for robotics and computer vision. Popular approaches for solving this task are image-based methods. Nowadays, these methods ha…
MeSLAM: Memory Efficient SLAM based on Neural Fields
Evgenii Kruzhkov, Alena Savinykh, Pavel Karpyshev +4
Existing Simultaneous Localization and Mapping (SLAM) approaches are limited in their scalability due to growing map size in long-term robot operation. Moreover, processing such ma…
MuCaSLAM: CNN-Based Frame Quality Assessment for Mobile Robot with Omnidirectional Visual SLAM
Pavel Karpyshev, Evgeny Kruzhkov, Evgeny Yudin +6
In the proposed study, we describe an approach to improving the computational efficiency and robustness of visual SLAM algorithms on mobile robots with multiple cameras and limited…
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…
DarkSLAM: GAN-assisted Visual SLAM for Reliable Operation in Low-light Conditions
Alena Savinykh, Mikhail Kurenkov, Evgeny Kruzhkov +4
Existing visual SLAM approaches are sensitive to illumination, with their precision drastically falling in dark conditions due to feature extractor limitations. The algorithms curr…