DDL-SLAM: A Robust RGB-D SLAM in Dynamic Environments Combined With Deep Learning

Ai, Yongbao; Rui, Ting; Lu, Mingyue; Fu, Lijun; Liu, Shuai; Wang, Song

doi:10.1109/access.2020.2991441

Cited by 60 publications

(41 citation statements)

References 37 publications

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“…Beshaw et al [100] and Williams et al [101] propose different architectures to accelerate the ICP algorithm, and Gautier et al [102] implemented the ICP and the volumetric integration algorithms in a heterogeneous architecture. Recent publications have focused on developing robust RGB-D SLAM algorithms considering dynamic environments conditions [103][104][105].…”

Section: Input Datamentioning

confidence: 99%

A Comprehensive Survey of Visual SLAM Algorithms

et al. 2022

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Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.

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Section: Input Datamentioning

confidence: 99%

A Comprehensive Survey of Visual SLAM Algorithms

et al. 2022

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“…All dynamic regions are deleted in each frame before fusing them into the overall static image frame. DDL-SLAM [ 35 ] is an RGB-D SLAM system for dynamic environments. DDL-SLAM adopts deformable U-Net [ 36 ] to provide pixel-wise semantic segmentation followed by multi-view geometry to verify if an object is dynamic.…”

Section: Related Workmentioning

confidence: 99%

DOE-SLAM: Dynamic Object Enhanced Visual SLAM

Lang

2021

Sensors

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In this paper, we formulate a novel strategy to adapt monocular-vision-based simultaneous localization and mapping (vSLAM) to dynamic environments. When enough background features can be captured, our system not only tracks the camera trajectory based on static background features but also estimates the foreground object motion from object features. In cases when a moving object obstructs too many background features for successful camera tracking from the background, our system can exploit the features from the object and the prediction of the object motion to estimate the camera pose. We use various synthetic and real-world test scenarios and the well-known TUM sequences to evaluate the capabilities of our system. The experiments show that we achieve higher pose estimation accuracy and robustness over state-of-the-art monocular vSLAM systems.

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“…Ting Sun et al [44] proposed a movable object aware SLAM system via weakly supervised semantic segmentation, and the main advantage of this system is that it avoids expensive annotations for training. DDL-SLAM [45] detects dynamic objects with semantic masks obtained by DUNet [46] and multi-view geometry, and then reconstructs the background obscured by dynamic objects with the strategy of image inpainting.…”

Section: Related Work a Dynamic Slammentioning

confidence: 99%

Semantic SLAM With More Accurate Point Cloud Map in Dynamic Environments

et al. 2020

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Static environment is a prerequisite for most existing vision-based SLAM (simultaneous localization and mapping) systems to work properly, which greatly limits the use of SLAM in real-world environments. The quality of the global point cloud map constructed by the SLAM system in a dynamic environment is related to the camera pose estimation and the removal of noise blocks in the local point cloud maps. Most dynamic SLAM systems mainly improve the accuracy of camera localization, but rarely study on noise blocks removal. In this paper, we proposed a novel semantic SLAM system with a more accurate point cloud map in dynamic environments. We obtained the masks and bounding boxes of the dynamic objects in the images by BlitzNet. The mask of a dynamic object was extended by analyzing the depth statistical information of the mask in the bounding box. The islands generated by the residual information of dynamic objects were removed by a morphological operation after geometric segmentation. With the bounding boxes, the images can be quickly divided into environment regions and dynamic regions, so the depth-stable matching points in the environment regions are used to construct epipolar constraints to locate the static matching points in the dynamic regions. In order to verify the preference of our proposed SLAM system, we conduct the experiments on the TUM RGB-D datasets. Compared with the state-of-theart dynamic SLAM systems, the global point cloud map constructed by our system is the best. INDEX TERMS dynamic environment, global point cloud map, noise blocks, semantic SLAM.

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DDL-SLAM: A Robust RGB-D SLAM in Dynamic Environments Combined With Deep Learning

Cited by 60 publications

References 37 publications

A Comprehensive Survey of Visual SLAM Algorithms

A Comprehensive Survey of Visual SLAM Algorithms

DOE-SLAM: Dynamic Object Enhanced Visual SLAM

Semantic SLAM With More Accurate Point Cloud Map in Dynamic Environments

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