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A Novel Method for Distinguishing Indoor Dynamic and Static Semantic Objects Based on Deep Learning and Space Constraints in Visual-inertial SLAM

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Abstract

Since the moving objects can degrade the stability of the visual-inertial simultaneous localization and mapping (VI-SLAM) systems significantly, this paper proposes a method based on deep learning and space constraints to distinguish the dynamic and static semantic objects. In detail, a pre-trained object detector predicts bounding boxes and class probabilities from selected keyframes. Then, a proposed random-sampling clustering algorithm, R-DBSCAN, filters the outliers lying within the prefiltered bounding boxes. After calculating the centroid of the remaining feature points, the semantic object’s attributes are judged by a proposed strategy: if the dynamic probability of the semantic object is larger than the static probability, the semantic object is dynamic; otherwise, it is static. Additionally, the drift errors of the VI-SLAM is constrained by the pedestrian dead reckoning (PDR) velocity. A series of experiments are conducted using the self-collected dataset to evaluate the accuracy of the proposed method in distinguishing the attributes of semantic objects for application scenes. The results demonstrate that the proposed method achieves the highest precision, recall, and F1 score as compared to other state-of-the-art methods. Additionally, the centroid of the dynamic semantic object changes in continuous keyframes reflecting its motion trajectory relative to the pose graph.

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Data Availability 

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

custom code

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Funding

This work was supported by the Overseas Taishan Scholars Program.

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    Authors and Affiliations

    1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China

      Chao Li & Hao Yuan

    2. College of Sino-German Institute Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

      Wennan Chai, Mingyue Zhang, Fanyu Lin & Qingdang Li

    3. College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China

      Sha Luo

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    1. Chao Li
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    Contributions

    C.L., W.C., and H.W. designed the algorithm. C.L., M.Z., F.L., and S.L. performed the experiments and analyzed the data. C.L. and Q.L. developed the source code and wrote the draft. All the author revised the manuscript.

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    Correspondence to Qingdang Li.

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    Chao Li and Wennan Chai contributed equally to this work.

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    Li, C., Chai, W., Zhang, M. et al. A Novel Method for Distinguishing Indoor Dynamic and Static Semantic Objects Based on Deep Learning and Space Constraints in Visual-inertial SLAM. J Intell Robot Syst 106, 26 (2022). https://doi.org/10.1007/s10846-022-01730-0

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