A Closed-loop Detection Algorithm for Online Updating of Bag-Of-Words Model

Shen, Xinquan; Chen, Lihang; Hu, Zhuhua; Fu, Yuexin; Hao, Qian; Xiang, Yunfeng; Wu, Jiaqi

doi:10.1145/3589845.3589847

Cited by 5 publications

(1 citation statement)

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“…Several studies enhanced VSLAM algorithms by refining the bag-of-words model [ 16 ]. Shen, et al [ 17 ] proposed a loop closure detection algorithm based on an enhanced real-time updating bag-of-words model. By extracting feature descriptors from online images and integrating them with preloaded offline words, a customized bag-of-words specific to the mobile robot’s operational environment was generated.…”

Section: Key Technologies and Influencing Factors Of Vslammentioning

confidence: 99%

A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation

Liu,

He,

et al. 2024

Sensors

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Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.

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Section: Key Technologies and Influencing Factors Of Vslammentioning

confidence: 99%