doi:10.16186/j.cnki.1673-9787.2020020053

Received:2020/02/242

Revised:2020/07/09

Published:2021/07/15

A visual SLAM algorithm based on improved word bag model

ZHANG Guangyao, NI Yihua, LYU Yan, NI Zhongjin, HUANG Tongjiao

School of Engineering， Zhej'iang A&F University， Hangzhou 310000 ， Zhejiang， China

Abstract:Aiming at the problems of low accuracy and poor practicability of visual SLAM in indoor environment ，by using depth camera as the sensor， an algorithm of visual SLAM was proposed based on improved word bag model. The traditional word bag model was improved by increasing the distance between nodes， and octree method was use to transform point cloud to generate octree map which could be used for navigation. Then a comparative experiment between the improved word bag model and the original word bag model， three data set accuracy experiments and a field experiment were carried. The experimental results showed that the improved word bag model was more powerful in similarity calculation and discrimination， the SLAM algorithm worked well when the camera motion was slow and the environment had loop. The requirements of indoor positioning， mapping and subsequent navigation were satisfied.

Key words:visual SLAM;loop closure detection;word bag model;octomap;TUM dataset

 一种基于改进词袋模型的视觉SLAM算法_张光耀.pdf