This work details a new method for loop-closure detection based on using multiple orthogonal projections to generate a global signature for each image of a video sequence. The new multi-projection function permits the detection of images corresponding to the same scene, but taken from different point of views. The signature generation process preserves enough information for robust loop-closure...

In recent years, the robotics community has extensively examined methods concerning place recognition task within scope of simultaneous localization and mapping applications. This article proposes an appearance-based loop closure detection pipeline named “Fast Incremental Loop Detection (FILD++). First, system is fed by consecutive images and, via passing them twice through a single convolution...

Abstract Loop closure detection (LCD) plays an important role in visual simultaneous location and mapping (SLAM), as it can effectively reduce the cumulative errors of SLAM system after a long period movement. Convolutional neural networks (CNNs) have significant advantage image similarity comparison, researchers achieved good results by incorporating CNNs into LCD. The LCD based on CNN is more...

Appearance-based localization can provide loop closure detection at vast scales regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale not only with the size of the environment but also with the operation time of the platform. Additionally, repeated visits to locations will develop multiple competing representation...

Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are usually affected by the illumination changes and texture changes under different weather conditions. Traditional methods in LCD usually rely on handcraft features, however, such methods are unable to capture the common ...

Loop closure detection is an essential component of simultaneous localization and mapping (SLAM) systems, which reduces the drift accumulated over time. Over years, several deep learning approaches have been proposed to address this task; however, their performance has subpar compared handcrafted techniques, especially while dealing with reverse loops. In article, we introduce novel loop networ...

Dear Editor, Loop closure detection (LCD) is an important module in simultaneous localization and mapping (SLAM). In this letter, we address the LCD task from semantic aspect to geometric one. To end, a network termed as AttentionNetVLAD which can simultaneously extract global local features proposed. It leverages attentive selection for features, coupling with reweighting soft assignment NetVL...

A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory reduce drift accumulated over time from odometry. Most LiDAR-based methods achieve this goal by using only geometric information, disregarding semantics scene. In work, we introduce PADLoC for joint closure detection and registration frameworks. We propose novel transformer-based head point cloud ...

Loop closure detection is a crucial part of VSLAM. However, the traditional loop algorithms are difficult to adapt complex and changeable scenes. In this paper, we fuse Gist features, semantic features appearance image detect closures quickly accurately. Firstly, take advantage fast extraction speed feature by using it screen candidate frames. Then, current frame semantically segmented obtain m...

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization mapping (SLAM) system. Typically, a SLAM system consists of front-end odometry back-end optimization modules. Loop closure pose graph are key factors determining performance However, works at single wavelength (905 nm), few textures or visual features extracted, which restricts point cloud...