A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also fa...A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also facilitates the detection of dynamic and hollowed-out obstacles. Essentially using this method, an improved clustering algorithm based on fast search and discovery of density peaks （CBFD） is presented to extract various obstacles in the environment map. By comparing with other cluster algorithms, CBFD can obtain a favorable number of clusterings automatically. Furthermore, the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm （ISODATA）.展开更多
The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory strea...The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.展开更多
基金Supported by the National Natural Science Foundation of China(61103157)
文摘A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also facilitates the detection of dynamic and hollowed-out obstacles. Essentially using this method, an improved clustering algorithm based on fast search and discovery of density peaks （CBFD） is presented to extract various obstacles in the environment map. By comparing with other cluster algorithms, CBFD can obtain a favorable number of clusterings automatically. Furthermore, the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm （ISODATA）.
基金This work is supported by the National Natural Science Foundationof China under Grants No. 41471371.
文摘The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.
