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A Semi-Direct Monocular Visual SLAM Algorithm in Complex Environments

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Abstract

A novel monocular visual simultaneous localization and mapping (SLAM) algorithm built on the semi-direct method is proposed to deal with some problems in complex environments, such as low-texture, moving objects and perceptual aliasing. The proposed algorithm takes advantage of direct and feature-based methods. On one hand, a direct method is used to track the camera poses and solve the feature alignment. On the other hand, ORB features in keyframes are extracted and matched for optimization and loop closure. To improve the localization accuracy in dynamic environments, a motion detection module that is robust to illumination change is adopted. In addition, for the sake of resolving the loop closure detection problem in perceptual aliasing scenes, this paper fuses the spatial information between two visual words into the bag of visual words (BoVW) model and employs an improved pyramid term frequency-inverse document frequency (TF-IDF) scoring match scheme. Experimental results prove that the proposed algorithm behaves better performance than ORB-SLAM with regard to overall accuracy and speed in complex environments.

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Acknowledgments

This work is supported by National Nature Science Foundation (Grant No.61573100) and NJUPT Program (Grant No. NY219123). We sincerely acknowledge constructive comments from editors and reviewers.

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  1. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China

    Zhiwei Liang & Chengzhi Wang

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  1. Zhiwei Liang
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  2. Chengzhi Wang
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Correspondence to Chengzhi Wang.

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Liang, Z., Wang, C. A Semi-Direct Monocular Visual SLAM Algorithm in Complex Environments. J Intell Robot Syst 101, 25 (2021). https://doi.org/10.1007/s10846-020-01297-8

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