CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynam...

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Bibliographic Details
Published in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 4399 - 4406
Main Authors Hu, Xinggang, Zhang, Yunzhou, Cao, Zhenzhong, Ma, Rong, Wu, Yanmin, Deng, Zhiqiang, Sun, Wenkai
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.10.2022
Subjects
Clustering algorithms
Heuristic algorithms
Location awareness
Pose estimation
Semantics
Simultaneous localization and mapping
Visualization
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Summary:The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9981826