Visual slam: What are the current trends and what to expect?
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown
significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous …
significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous …
Visual and Visual‐Inertial SLAM: State of the Art, Classification, and Experimental Benchmarking
M Servières, V Renaudin, A Dupuis… - Journal of …, 2021 - Wiley Online Library
Simultaneous Localization and Mapping is now widely adopted by many applications, and
researchers have produced very dense literature on this topic. With the advent of smart …
researchers have produced very dense literature on this topic. With the advent of smart …
RDS-SLAM: Real-time dynamic SLAM using semantic segmentation methods
The scene rigidity is a strong assumption in typical visual Simultaneous Localization and
Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in …
Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in …
FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fusion
Semantic segmentation of urban scenes is an essential component in various applications
of autonomous driving. It makes great progress with the rise of deep learning technologies …
of autonomous driving. It makes great progress with the rise of deep learning technologies …
RDMO-SLAM: Real-time visual SLAM for dynamic environments using semantic label prediction with optical flow
Visual simultaneous localization and mapping (vSLAM) are considered a fundamental
technology for augmented reality and intelligent mobile robots. However, rigid scene …
technology for augmented reality and intelligent mobile robots. However, rigid scene …
Dgs-slam: A fast and robust rgbd slam in dynamic environments combined by geometric and semantic information
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to
accomplish fully autonomous movement and exploration in unknown environments. At …
accomplish fully autonomous movement and exploration in unknown environments. At …
Real-time dynamic SLAM algorithm based on deep learning
P Su, S Luo, X Huang - IEEE Access, 2022 - ieeexplore.ieee.org
In the traditional visual simultaneous localization and mapping (SLAM), the strong static
assumption leads to a large degradation in the accuracy of visual SLAM in dynamic …
assumption leads to a large degradation in the accuracy of visual SLAM in dynamic …
Mpi-flow: Learning realistic optical flow with multiplane images
The accuracy of learning-based optical flow estimation models heavily relies on the realism
of the training datasets. Current approaches for generating such datasets either employ …
of the training datasets. Current approaches for generating such datasets either employ …
Robust SLAM systems: Are we there yet?
Progress in the last decade has brought about significant improvements in the accuracy and
speed of SLAM systems, broadening their mapping capabilities. Despite these …
speed of SLAM systems, broadening their mapping capabilities. Despite these …
An improved adaptive ORB-SLAM method for monocular vision robot under dynamic environments
J Ni, X Wang, T Gong, Y Xie - International Journal of Machine Learning …, 2022 - Springer
The vision-based simultaneous localization and mapping (SLAM) method is a hot spot in the
robotic research field, and Oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one …
robotic research field, and Oriented FAST and Rotated BRIEF (ORB) SLAM algorithm is one …