Stereo camera visual SLAM with hierarchical masking and motion-state classification at outdoor construction sites containing large dynamic objects

Bao, Runqiu; Komatsu, Ren; Miyagusuku, Renato; Chino, Masaki; Yamashita, Atsushi; Asama, Hajime

doi:10.1080/01691864.2020.1869586

Cited by 14 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new Ant colony algorithm was applied to search dynamic points quickly to reduce the computation time. Bao et al (2021aBao et al ( , 2021b) used semantic information combined with object-level geometric constraints to detect the static parts of the scene and perform self-motion tracking. Wen et al (2021) applied Mask-RCNN to segment objects, using depth error, photometric error and reprojection error to identify dynamic objects and static objects.…”

Section: Related Workmentioning

confidence: 99%

“…MobileNetv2, which is a lightweight network that operates fast and accurately, is used to extract input into Deeplabv31 network for prediction, and the result is shown in Figure 2. The network can divide the whole image into eight categories and 34 classes (Bao et al, 2021a(Bao et al, , 2021b. We are only interested in five categories and 15 classes.…”

Section: Semantic Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

Song

Hao

et al. 2023

View full text Add to dashboard Cite

Purpose The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation. Design/methodology/approach First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions. Findings The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments. Originality/value The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Semantic Segmentationmentioning

confidence: 99%

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

Song

Hao

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It can eliminate dynamic objects combined with a selective tracking algorithm. Bao et al [28] proposes a motion segmentation method to efficiently extract static parts from crowded dynamic scenes to enable the robust tracking of camera egomotion.…”

Section: Visual Slam In a Dynamic Environmentmentioning

confidence: 99%

DyStSLAM: an efficient stereo vision SLAM system in dynamic environment

Li¹,

Shen²,

Lu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Simultaneous Localization and Mapping(SLAM) is the basis for many robotic applications. Most SLAM algorithms are based on the assumption that the scene is static. In real-world applications, moving objects are inevitable, which will greatly impact the ego-pose estimation accuracy. This paper presents DyStSLAM, a visual SLAM system with stereo configuration that can efficiently identify moving objects and accomplish dynamic data association. First of all, DyStSLAM extracts feature points, estimates disparity map, and performs instance segmentation simultaneously. Then, the obtained results are combined to estimate the motion confidence and discriminate between moving objects and static ones. A confidence based matching algorithm is proposed to associate dynamic objects and estimate the pose of each moving object. At the same time, static objects are used to estimate the pose of the camera. Finally, after nonlinear optimization, a sparse point cloud map of both static background and dynamic objects is constructed. Compared with ORB-SLAM2, the proposed method outperforms by 31% for ATE on KITTI dataset.

show abstract

An RGB-D SLAM algorithm based on adaptive semantic segmentation in dynamic environment

Song

2023

J Real-Time Image Proc

View full text Add to dashboard Cite

Stereo camera visual SLAM with hierarchical masking and motion-state classification at outdoor construction sites containing large dynamic objects

Cited by 14 publications

References 22 publications

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

Semantic stereo visual SLAM toward outdoor dynamic environments based on ORB-SLAM2

DyStSLAM: an efficient stereo vision SLAM system in dynamic environment

An RGB-D SLAM algorithm based on adaptive semantic segmentation in dynamic environment

Contact Info

Product

Resources

About