An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

Wang, Chunxiao; Ji, Min; Wang, Jian; Wen, Wei; Li, Ting; Sun, Yong

doi:10.3390/s19010172

Cited by 81 publications

(52 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DBSCAN is a density-based clustering algorithm proposed by Sander, J. et al [26] in 1998, which is widely used in the fields of physics [27], computer science [28,29], medicine [30], architecture [31], agriculture [32] and so on. Compared to other clustering methods such as K-means and Gaussian mixtures, the advantages of the DBSCAN method lie in the following aspects: (1) It has better identification capability for abnormal points.…”

Section: Diagnosis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Zhang

Liu

et al. 2019

Energies

View full text Add to dashboard Cite

Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

show abstract

Section: Diagnosis Methodsmentioning

confidence: 99%

“…In order to compare the effects of different fault diagnosis methods, the fault frequencies of the cells in vehicles 1~2 and 5~12 are calculated by 3r-MSS fault diagnosis method [28]. Figure 12 shows the results of vehicles 1~2 and 5~6.…”

Section: Comparison With Other Diagnosis Methodsmentioning

confidence: 99%

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Zhang

Liu

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

“…However, little but some research was done with these algorithms on LiDAR sensor data. Eps estimation with DBSCAN for LIDAR data [12] was influential research. Here they concentrated on automatic point cloud segmentation and parameter estimation for DBSCAN.…”

Section: Related Work In Cluster-based Trackingmentioning

confidence: 99%

Person Tracking Using Ankle‐Level LiDAR Based on Enhanced DBSCAN and OPTICS

Hasan

Hanawa

Goto

et al. 2021

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Along with the progress of deep learning techniques, people tracking using video cameras became easy and accurate. However, privacy and security issues are not enough to be concerned with vision-based monitoring. People may not be tolerated surveillance cameras installed everywhere in our daily life. A camera-based system may not work robustly in unusual situations such as smoke, fogs, or darkness. To cope with these problems, we propose a two-dimensional (2D) LiDAR-based people tracking technique based on clustering algorithms. A LiDAR sensor is a prominent approach for tracking people without disclosing their identity, even under challenging conditions. For tracking people, we propose modified density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify cluster structure (OPTICS) algorithms for clustering 2D LiDAR data. We have confirmed that our approach significantly improves the accuracy and robustness of people tracking through the experiments.

show abstract

“…In the field of computer vision, some previous works propose different approaches for plant phenotyping considering the spectral response [15], the recognition of different geological formations using a multispectral camera [16] and the detection of urban materials [17,18]. Other recent approaches have proposed a semantic classification of urban spaces by applying neural networks [19,20] or using LiDAR data and a clustering approach [21]. Furthermore, the use of deep learning techniques for unsupervised scenarios is a promising research [22,23].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Jurado

Cárdenas

Ogáyar

et al. 2020

Sensors

View full text Add to dashboard Cite

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

show abstract

An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

Cited by 81 publications

References 43 publications

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Person Tracking Using Ankle‐Level LiDAR Based on Enhanced DBSCAN and OPTICS

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Contact Info

Product

Resources

About