Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (9): 2666-2675.doi: 10.13229/j.cnki.jdxbgxb.20211293

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Loop detection based on uniform ORB

Mian-shu CHEN1(),Lu-lu YU1,Xiao-ni LI1,Hong-yu ZHENG2   

  1. 1.College of Communication Engineering,Jilin University,Changchun 130022,China
    2.The State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
  • Received:2021-11-29 Online:2023-09-01 Published:2023-10-09

Abstract:

Aiming to solve the aggregation problem of traditional uniform oriented FAST and rotated BRIEF(ORB) features in visual simultaneous localization and mapping(SLAM), a uniform FAST corner extraction method is designed, which is based on grid division and laying to determinate key points. Furthermore, a corresponding loop detection method is designed based on uniform distribution of ORB features combined with brute force matching. Experiment results compared with BoW-based loop detection algorithms show that the proposed algorithm can significantly improve the accuracy of loop detection. Furthermore, a robot operating system(ROS) based loosely coupled semi-direct SLAM system is designed, which combine the uniform ORB feature loop detection module with direct sparse odometry(DSO). The experimental results show that the proposed system has high map construction performance.

Key words: information processing technology, simultaneous localization and mapping(SLAM), loop detection, uniform oriented FAST and rotated BRIEF(ORB), brute force matching, direct sparse odometry(DSO)

CLC Number: 

  • TP391.4

Fig.1

FAST corner"

Fig.2

FAST corner distribution in OpenCV"

Fig.3

Uniform FAST corner distribution"

Fig.4

Schematic diagram of different place"

Fig.5

Schematic diagram of matching at same place"

Fig.6

Schematic diagram of Npcollection"

Fig.7

Visual SLAM composition block diagram"

Table 1

Experiment environment"

处理器AMD Ryzen7 4800H
显卡NVIDIA RTX2060
内存Samsung DDR4 16 GB
固态硬盘Western Digital 512 GB
操作系统Ubuntu 18.04 LTS

Fig.8

Datasets example images"

Fig.9

Example images of TUM_Mono dataset"

Fig.10

AUC comparison of two algorithms"

Table 2

Time performance on NewCollege of two algorithms"

算法

特征编码

时间/ms

特征匹配

时间/ms

相似度计算

时间/ms

均匀ORB16.2115.170.01
基于BoW14.09-0.05

Table 3

Time performance on CityCentre of two algorithms"

算法

特征编码

时间/ms

特征匹配

时间/ms

相似度计算

时间/ms

均匀ORB17.3816.300.01
基于BoW15.25-0.05

Fig.11

Comparison of successful runs"

Fig.12

Comparison of generated trajectories"

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