Moving Humans Removal for Dynamic Environment Reconstruction from Slow-Scanning LIDAR Data

Zhang, Tianwei; Nakamura, Yoshihiko

doi:10.1109/urai.2018.8441778

Cited by 8 publications

(7 citation statements)

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“…are built. The 3D LiDARs in these studies are fully DIY , or a mirror or prism is used to change the optical path of the laser beams [41,42,89]. Specifically, in [86], two prisms are utilized to project the laser beam in a large range in the vertical direction, as the result of which, a DIY 2D LiDAR is constructed.…”

Section: Extra 2: Diy Low-cost 3d Lidar and A Rotating Mirror/prismmentioning

confidence: 99%

“…in the environment. However, compared to a camera, a moving 2D LiDAR is much slower in 3D scanning of the environment [42]. If a moving 2D LiDAR is used to scan a pedestrian walking through the field of view, the shape of the collected 3D point cloud may not be a human body, but a ribbon.…”

Section: Problem 23-for Categories 1 To 6: the Distortion Of The 3d P...mentioning

confidence: 99%

“…When it comes to the solution of how to deal with moving objects in the environment with the use of a moving 2D LiDAR, there are few related studies. In [42], the method to model the dynamic environment by a slow scanning LiDAR (that is, the frequency of 3D scanning is lower than 1 Hz) was studied. The prototype used is this literature is a pitching 2D LiDAR.…”

Section: Problem 23-for Categories 1 To 6: the Distortion Of The 3d P...mentioning

confidence: 99%

“…Therefore, when there are moving objects in the environment, a moving 2D LiDAR is not likely to capture their shapes. Therefore, for a moving 2D LiDAR, the recognition and removal of moving objects is a problem that needs to be solved [42,43]. For a commercialized 3D LiDAR, the process of moving objects is simple and mature [44][45][46][47], but for a moving 2D LiDAR, the process of moving objects is much more difficult and relatively unexplored, because the shape of a moving object is difficult to be accurately captured by a moving 2D LiDAR.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Low-Cost 3D Laser Scanning Technology

Yuan

Liu

et al. 2021

Applied Sciences

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By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.

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Section: Extra 2: Diy Low-cost 3d Lidar and A Rotating Mirror/prismmentioning

confidence: 99%

Section: Problem 23-for Categories 1 To 6: the Distortion Of The 3d P...mentioning

confidence: 99%

Section: Problem 23-for Categories 1 To 6: the Distortion Of The 3d P...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of Low-Cost 3D Laser Scanning Technology

Yuan

Liu

et al. 2021

Applied Sciences

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“…Recently, 3D virtual environments have been built and used in fields that require learning or verification in various situations, such as autonomous driving or remote monitoring [1][2][3][4]. The virtual environment can be reconstructed to provide situations that can occur in the real environment using the 3D point cloud measured by light detection and range (LiDAR) [5][6][7][8]. The movement of virtual objects, such as a person, is expressed by using divided moving objects based on the measured 3D point cloud [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

Kwak

Sung

2021

Remote Sensing

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Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.

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