基于原图-光照不变图视觉词典改进的闭环检测方法  

A Method of Loop Closure Detection Improved by Bag-of-Visual Words Based on Original-Illumination Invariant Image

作　　者：胡章芳[1] 曾念文 罗元[1] 肖雨婷 钟征源 HU Zhang-fang;ZENG Nian-wen;LUO Yuan;XIAO Yu-ting;ZHONG Zheng-yuan(School of Optoelectronic Engineering,Chongqing University of Posts and Telecommunications,Nan'an ChongQing,400065)

机构地区：[1]重庆邮电大学光电工程学院,重庆南岸区400065

出　　处：《电子科技大学学报》2021年第4期586-591,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金青年基金(67103067);重庆市教育委员会科学技术研究项目(KJ1704072)。

摘　　要：当机器人所处环境光照发生变化时,基于传统视觉词典的闭环检测算法性能会降低,容易出现感知混叠和感知变异,从而判断出假闭环。该文首先通过原彩色图像生成只与光源有关的光照不变图,然后生成原图−光照不变图的视觉词典,对每帧图像计算两个直方图和相似性得分,通过最终的得分矩阵来判断是否闭环。实验结果表明,与传统的视觉词典法相比,该文提出的闭环检测算法对环境的光照变化具有较好的鲁棒性。When the ambient light of the robot changes,the performance of the loop closure detection algorithm based on the traditional visual word bag will decrease,and it is prone to perceptual aliasing and perceptual variation,thus judging the false closed-loop.In this paper,the original color image is used to generate an illumination invariant image related only to the light source,and then a visual dictionary of the original illumination invariant image is generated.For each image,two histograms and similarity scores are calculated to determine whether it is a closed loop.Finally,it is tested on the data set.The experimental results show that compared with the bag-of-words(BoW),the loop closure detection algorithm proposed in this paper has better robustness to the changes in the environment.

关 键 词：视觉词袋 光照不变图 闭环检测 同步定位与建图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]