Localizing previously visited places during long-term localization and mapping, that is, loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. In common bag-of-words (BoW) model, visual vocabulary built associate features for detecting loops. Currently, methods build vocabularies off-line determine scales of the by trial-and-error, which results in unreaso...

Recognizing a previously visited place, also known as place recognition (or loop closure detection) is the key towards fully autonomous mobile robots and self-driving vehicle navigation. Augmented with various Simultaneous Localization and Mapping techniques (SLAM), loop closure detection allows for incremental pose correction and can bolster efficient and accurate map creation. However, repeat...

Herein, a real-time, fast, tightly coupled simultaneous localization and mapping (SLAM) system is proposed. The deep neural network used to segment the point cloud semantically construct semantic map descriptor, global navigation satellite real-time kinematic position detect loop closure. Finally, factor-graph model for optimization. working principle of SLAM introduced in detail, including con...

We address the problem of navigation in topometric maps created by using odometry data and visual loopclosure detection. Based on our previous work [6], we present an optimized version of our loop-closure detection algorithm that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated. Using such a map, the proposed navigation algorithm performs qual...

Loop closure detection is an essential component for simultaneously localization and mapping in a variety of robotics applications. One of the most challenging problems is to perform long-term place recognition with strong perceptual aliasing and appearance variations due to changes of illumination, vegetation, weather, etc. To address this challenge, we propose a novel Robust Multimodal Sequen...

Loop closure detection serves as the fulcrum of improving accuracy and precision in simultaneous localization mapping (SLAM). The majority loop methods extract artificial features, which fall short learning comprehensive data information, but unsupervised a typical deep method excels self-access clustering to analyze similarity without handling data. Moreover, does solve restrictions on image q...