Loop Closure Detection Based on Convolutional Neural Network
LUO Shunxin
ZHANG Sunjie
Abstract：This paper focuses on the problem of loop closure detection in mobile robots during visual positioning and mapping. Loop closure detection is one of the most important parts of visual SLAM. During the movement of the robot,the robot realizes posi?tioning and mapping by estimating its own posture and sensing the surrounding environment. Since the robot uses the inter-frame pose estimation when estimating the pose,the estimation of the pose is drifted over time. Loop closure detection is aimed at solving the pose drift problem. Nowadays,the more popular method is to use the artificially built features and use the visual word bag meth?od to achieve loopback detection. This paper proposes a loopback detection method based on deep learning for convolutional neural networks. The mobile robot acquires the data of the visual image through the sensor,inputs it into the trained convolutional neural network,uses the convolution feature as the description of the image,and then processes the extracted feature to calculate the simi?larity score of the image. Finally,the validity of the verification algorithm is performed using the local dataset and the TUM dataset.
Keywords：visual SLAMloop closure detectiondeep learningpose driftconvolutional neural network
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1020-1026,1048 )