Simultaneous localization and mapping (SLAM) techniques are widely researched, since
they allow the simultaneous creation of a map and the sensors' pose estimation in an …

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 …

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 …

Semantic segmentation is a fundamental capability for autonomous vehicles. With the
advancements of deep learning technologies, many effective semantic segmentation …

When working in dynamic environment, traditional SLAM framework performs poorly due to
interference from dynamic objects. By taking advantages of deep learning in object …

Static environment is a prerequisite for most of visual simultaneous localization and
mapping systems. Such a strong assumption limits the practical application of most existing …

Underwater scene is highly unstructured, full of various noise interferences. Moreover, GPS
information is not available in the underwater environment, which thus brings huge …

Simultaneous localization and mapping (SLAM) is one of the fundamental capabilities for
intelligent mobile robots to perform state estimation in unknown environments. However …

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 …

Abstract Simultaneous Localization and Mapping (SLAM) systems are proposed to estimate
robot poses and reconstruct 3-D map of surrounding environment. Since most of the existing …