1 citations · 4 across the 7 of their papers we have counts for
7 papers
Hierarchical Pose Estimation and Mapping with Multi-Scale Neural Feature Fields
Evgenii Kruzhkov, Alena Savinykh, Sven Behnke
Robotic applications require a comprehensive understanding of the scene. In recent years, neural fields-based approaches that parameterize the entire environment have become popula…
RoboCup@Home 2024 OPL Winner NimbRo: Anthropomorphic Service Robots using Foundation Models for Perception and Planning
Raphael Memmesheimer, Jan Nogga, Bastian Pätzold +8
We present the approaches and contributions of the winning team NimbRo@Home at the RoboCup@Home 2024 competition in the Open Platform League held in Eindhoven, NL. Further, we desc…
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…
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…