Meng Song

YNICL Journal 2023 Journal Article

Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia

• Yong Han
• Yongfeng Yang
• Zhilu Zhou
• Xueyan Jin
• Han Shi
• Minglong Shao
• Meng Song
• Xi Su

YNICL Journal 2022 Journal Article

Abnormal patterns of regional homogeneity and functional connectivity across the adolescent first-episode, adult first-episode and adult chronic schizophrenia

• Yongfeng Yang
• Yuqing Sun
• Yuliang Zhang
• Xueyan Jin
• Zheng Li
• Minli Ding
• Han Shi
• Qing Liu

IROS Conference 2012 Conference Paper

Natural feature based localization in forested environments

• Meng Song
• Fengchi Sun
• Karl Iagnemma

This paper presents a new feature based scan matching method for solving 6D localization problem in forested environments. The proposed registration process includes two steps. First, the largest group of approximately parallel tree trunk features is utilized to align successive scans along the five dimensions except z direction. Tree correspondences are established by matching point patterns which are abstracted from the position relationships of trees. The optimal 5D transformation is thus determined based on the axes of two key tree pairs which are selected by evaluating their ability of tree alignment. Second, we assign the ground points of two scans into a grid of cells, and minimize z-direction difference of points in shared cells. The experimental results on data collected in real forested environments have demonstrated the effectiveness of this method.

ICRA Conference 2012 Conference Paper

Natural landmark extraction in cluttered forested environments

• Meng Song
• Fengchi Sun
• Karl Iagnemma

In this paper, a new systematical method for extracting tree trunk landmarks from 3D point clouds of cluttered forested environments is proposed. This purely geometric method is established on scene understanding and automatic analysis of trees. The pipeline of our method includes three steps. First, the raw point clouds are segmented by utilizing the circular shape of trees, and segments are grouped into tree sections based on the principle of spatial proximity. Second, circles and axes are extracted from tree sections which are subject to loss of shape information. Third, by clustering and integrating the tree sections resulted from various space inconsistencies, straight tree trunk landmarks are finally formed for future localization. The experimental results from real forested environments are presented.