RLD-SLAM: A Robust Lightweight VI-SLAM for Dynamic Environments Leveraging Semantics and Motion Information

Zheng, Zengrui; Lin, Shifeng; Yang, Chenguang

doi:10.1109/tie.2024.3363744

Cited by 7 publications

References 35 publications

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Visual Inertial SLAM Based on Spatiotemporal Consistency Optimization in Diverse Environments

Pu,

Luo,

Wang

et al. 2024

Journal of Field Robotics

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Currently, the majority of robots equipped with visual‐based simultaneous mapping and localization (SLAM) systems exhibit good performance in static environments. However, practical scenarios often present dynamic objects, rendering the environment less than entirely “static.” Diverse dynamic objects within the environment pose substantial challenges to the precision of visual SLAM system. To address this challenge, we propose a real‐time visual inertial SLAM system that extensively leverages objects within the environment. First, we reject regions corresponding to dynamic objects. Following this, geometric constraints are applied within the stationary object regions to elaborate the mask of static areas, thereby facilitating the extraction of more stable feature points. Second, static landmarks are constructed based on the static regions. A spatiotemporal factor graph is then created by combining the temporal information from the Inertial Measurement Unit (IMU) with the semantic information from the static landmarks. Finally, we perform a diverse set of validation experiments on the proposed system, encompassing challenging scenarios from publicly available benchmarks and the real world. Within these experimental scenarios, we compare with state‐of‐the‐art approaches. More specifically, our system achieved a more than 40% accuracy improvement over baseline method in these data sets. The results demonstrate that our proposed method exhibits outstanding robustness and accuracy not only in complex dynamic environments but also in static environments.

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Visual Inertial SLAM Based on Spatiotemporal Consistency Optimization in Diverse Environments

Pu,

Luo,

Wang

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

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Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot

Chen,

Song,

Wang

et al. 2025

Automation in Construction

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A semantic visual SLAM based on improved mask R-CNN in dynamic environment

Zhang,

Dong,

Guo

et al. 2024

Robotica

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To address the issues of low positioning accuracy and weak robustness of prior visual simultaneous localization and mapping (VSLAM) systems in dynamic environments, a semantic VSLAM (Sem-VSLAM) approach based on deep learning is proposed in this article. The proposed Sem-VSLAM algorithm adds semantic segmentation threads in parallel based on the open-source ORB-SLAM2’s visual odometry. First, while extracting the ORB features from an RGB-D image, the frame image is semantically segmented, and the segmented results are detected and repaired. Then, the feature points of dynamic objects are eliminated by using semantic information and motion consistency detection, and the poses are estimated by using the remaining feature points after the dynamic feature elimination. Finally, a 3D point cloud map is constructed by using tracking information and semantic information. The experiment uses Technical University of Munich public data to show the usefulness of the Sem-VSLAM algorithm. The experimental results show that the Sem-VSLAM algorithm can reduce the absolute trajectory error and relative attitude error of attitude estimation by about 95% compared to the ORB-SLAM2 algorithm and by about 14% compared to the VO-YOLOv5s in a highly dynamic environment and the average time consumption of tracking each frame image reaches 61 ms. It is verified that the Sem-VSLAM algorithm effectively improves the robustness and positioning accuracy in high dynamic environment and owning a satisfying real-time performance. Therefore, the Sem-VSLAM has a better mapping effect in a highly dynamic environment.

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RLD-SLAM: A Robust Lightweight VI-SLAM for Dynamic Environments Leveraging Semantics and Motion Information

Cited by 7 publications

References 35 publications

Visual Inertial SLAM Based on Spatiotemporal Consistency Optimization in Diverse Environments

Visual Inertial SLAM Based on Spatiotemporal Consistency Optimization in Diverse Environments

Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot

A semantic visual SLAM based on improved mask R-CNN in dynamic environment

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