LIDAR-based dynamic environment modeling and tracking using particles based occupancy grid

Li, Qiaohui; Dai, Bin; Fu, Hao

doi:10.1109/icma.2016.7558567

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the recognition of moving objects of a moving 2D LiDAR, the method of identifying the leader in this literature may be worth learning. Besides, in [104][105][106], moving objects are recognized, according to the 2D maps collected by 2D LiDARs.…”

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

confidence: 99%

A Survey of Low-Cost 3D Laser Scanning Technology

Yuan

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

A Survey of Low-Cost 3D Laser Scanning Technology

Yuan

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Among the obstacle detection technologies used in front of self-driving vehicles, the most studied is sensor-based detection, including laser radar [3], millimeter wave radar [4] and vision sensors [5]. Compared with laser radar and millimeterwave radar, detection methods based on vision sensors have the advantages of low cost, convenient use and maintenance and good visualization effects, so they have been increasingly used in non-contact obstacle detection systems.…”

Section: Introductionmentioning

confidence: 99%

Detection method of obstacles in the dangerous area of electric locomotive driving based on MSE-YOLOv4-Tiny

Wang¹,

Wang²,

Guo³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Aiming at the problems of error warning, low detection efficiency, and inability to meet the requirements of lightweight deployment in current track obstacle detection algorithms based on computer vision, the detection method of obstacles in the dangerous area of electric locomotive driving based on improved YOLOV4-Tiny (MSE-YOLOV4-Tiny) was proposed. The obstacle image dataset was constructed to provide a testing environment for various target detection algorithms. The method of perspective transformation, sliding window and least square cubic polynomial was used to fit the track line. By finding the area where the track was located and extending a certain distance to the outside of the track, the dangerous area of the electric locomotive running was obtained. A 3-scale detection structure was formed by increasing the shallow detection scale on the detection layer, so as to improve the detection accuracy of the network for smaller targets such as stones. The improved SKNet (ECA_SKNet) attention mechanism module was added to the output ends of the three scales of the backbone network, and the weight was re-assigned to realize feature reconstruction, thus further improving the detection accuracy of the target. By adding the Spatial Pyramid Pooling (SPP) module, the local and global features of the image were fused to improve the accurate localization ability and detection accuracy of the network. A comparative experiment was carried out on the dataset constructed in this paper. The experimental results show that the problem of false warnings caused by taking the target in the safe area as an obstacle can be effectively solved by dividing the danger area of electric locomotive driving. Compared with the original YOLOv4-Tiny algorithm, the MSE-YOLOv4-Tiny algorithm has a 3.97% increase in mAP while maintaining a higher detection speed and a smaller model memory. It has better detection performance and can be used for autonomous driving electric locomotive obstacle detection to provide technical support.

show abstract

“…For detecting and tracking moving objects in more complex cases, an occupancy grid tracking system based on particles [ 17 ] has been proposed. The proposed occupancy grid tracking solution can be classified as using the Descartes probability model of the reverse sensor and it generates a fully dynamic grid.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor

Huang

Chen

Zhang

et al. 2017

Sensors

View full text Add to dashboard Cite

Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields.

show abstract

LIDAR-based dynamic environment modeling and tracking using particles based occupancy grid

Cited by 7 publications

References 14 publications

A Survey of Low-Cost 3D Laser Scanning Technology

A Survey of Low-Cost 3D Laser Scanning Technology

Detection method of obstacles in the dangerous area of electric locomotive driving based on MSE-YOLOv4-Tiny

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor

Contact Info

Product

Resources

About