A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM
نویسندگان
چکیده
Loop closure detection serves as the fulcrum of improving accuracy and precision in simultaneous localization mapping (SLAM). The majority loop methods extract artificial features, which fall short learning comprehensive data information, but unsupervised a typical deep method excels self-access clustering to analyze similarity without handling data. Moreover, does solve restrictions on image quality singleness semantics many traditional SLAM methods. Therefore, strategy based an is proposed this paper. main component adopts BigBiGAN features establish original bag words. Then, complete words used detect closing. Finally, considerable validation check ORB descriptor added verify result output outcome detection. algorithm other compared algorithms are, respectively, applied Autolabor Pro1 execute indoor visual SLAM. experiment shows that increases recall rate by 20% with ORB-SLAM2 LSD-SLAM. And it also improves at least 40.0% than others reduces 14% time loss ORB-SLAM2. presented benefit much environment.
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ژورنال
عنوان ژورنال: Journal of Electrical and Computer Engineering
سال: 2021
ISSN: ['2090-0155', '2090-0147']
DOI: https://doi.org/10.1155/2021/9978022