Dynam-SLAM: An Accurate, Robust Stereo Visual-Inertial SLAM Method in Dynamic Environments

Yin, Hesheng; Li, Shaomiao; Tao, Yu; Guo, Jun; Huang, Bo

doi:10.1109/tro.2022.3199087

Cited by 27 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Song et al [16] presented a robust bundle adjustment that could reject the features from dynamic objects by leveraging pose priors estimated by the IMU pre-integration. Yin et al [17] proposed a stereo visual-inertial SLAM system which loosely couples the stereo scene flow with IMU for dynamic feature detection and tightly couples the dynamic and static features with the IMU measurements for nonlinear optimization to make the system capable of robust, accurate and continuous work in high dynamic environment.…”

Section: Methods Based On the Camera Motion Modelmentioning

confidence: 99%

A visual SLAM method assisted by IMU and deep learning in indoor dynamic blurred scenes

Liu,

Cao,

Cheng

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Dynamic targets in the environment can seriously affect the accuracy of SLAM systems. This article proposes a novel dynamic visual SLAM method with IMU and deep learning for indoor dynamic blurred scenes, which improves the front end of ORB-SLAM2, combining deep learning with geometric constraint to make the dynamic feature points elimination more reasonable and robust. First, a multi-directional superposition blur augmentation algorithm is added to the YOLOv5s network to compensate for errors caused by fast-moving targets, camera shake and camera focus. Then, the fine-tuned YOLOv5s model is used to detect potential dynamic regions. Afterward, IMU measurements are introduced for rotation compensation to calculate the feature point velocity and estimate the motion speed of the camera, in order to estimate the real motion state of potential dynamic targets. Finally, real dynamic points will be removed and potential dynamic points will be reserved for subsequent pose estimation. Experiments are conducted on TUM dynamic dataset and in the real world. The results demonstrate that the proposed method has significant improvement compared with ORB-SLAM2, and has a more robust performance over some other state-of-the-art dynamic visual SLAM systems.

show abstract

Section: Methods Based On the Camera Motion Modelmentioning

confidence: 99%

A visual SLAM method assisted by IMU and deep learning in indoor dynamic blurred scenes

Liu,

Cao,

Cheng

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Jaafar and Andrey [29] used KMeans clustering with extreme constraints and SegNet for semantic segmentation to filter out features detected on moving objects, improving the real-time performance and accuracy of the SLAM system. Since the estimated trajectory of static landmarks is greatly different from that of all dynamic landmarks, Yin et al [30] proposed a method of loosely coupling the three-dimensional scene flow with the Inertial Measurement Unit (IMU) for dynamic feature detection and estimated the camera state by integrating IMU measurement and feature observation. Li et al [31] proposed object detection and scene flow feature-point-tracking technologies based on deep learning to separate and jointly optimize dynamic and static objects.…”

Section: Dynamic Target Detectionmentioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

View full text Add to dashboard Cite

Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

show abstract

“…Currently, most dynamic visual SLAM methods detect the dynamic objects in the current frame while estimating pose [11,48,49]. Since the pose of the current frame is not precise enough, the dynamic object recognition is not so smooth.…”

Section: Preprocessing In the Last Framementioning

confidence: 99%

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Liu,

Tian,

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Visual simultaneous localization and mapping (SLAM) is the underlying support of unmanned systems. Currently, most visual SLAM methods are based on the static environment assumption so that dynamic objects in the camera’s field of view will seriously disrupt its working performance. In view of this, an RGB-D SLAM approach based on probability observations and clustering optimization for highly dynamic environments is proposed, which can effectively eliminate the influence of dynamic objects and accurately estimate the ego-motion of an RGB-D camera. The method contains a dual static map point detection strategy, which is carried out simultaneously in the current and previous frames. First, to enhance tracking robustness in highly dynamic environments, the probabilities of map points being static, calculated by both reprojection deviation and intensity deviation, are used to weight the cost function for pose estimation. Meanwhile, by taking the previous frames as a reference, a static velocity probability based on sparse scene flow is acquired to preliminarily recognize static map points and further improve the tracking accuracy. Then, an improved map point optimization strategy based on K-means clustering is designed, which effectively takes advantage of the clustering algorithm to refine the static map point labels while alleviating its stubborn problem. Finally, the experimental results on the TUM dataset and real scenes compared with the state-of-the-art visual SLAM methods illustrate that the proposed method achieves an extremely robust and accurate performance for estimating camera pose in highly dynamic environments.

show abstract

Dynam-SLAM: An Accurate, Robust Stereo Visual-Inertial SLAM Method in Dynamic Environments

Cited by 27 publications

References 56 publications

A visual SLAM method assisted by IMU and deep learning in indoor dynamic blurred scenes

A visual SLAM method assisted by IMU and deep learning in indoor dynamic blurred scenes

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

Contact Info

Product

Resources

About