Liang Yang
City College of New York
40 Papers
91 Citations
Liang Yang is an academic researcher from City College of New York. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 9, co-authored 35 publications. Previous affiliations of Liang Yang include Shenyang Institute of Automation & City University of New York.
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Papers
Survey of Robot 3D Path Planning Algorithms
TL;DR: This paper discusses the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots.
A literature review of UAV 3D path planning
Liang Yang,Juntong Qi,Jizhong Xiao,Xia Yong +3 more
- 01 Jun 2014
TL;DR: This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years and classifies them into five categories, sampling-based algorithms, node-based algorithm, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms.
214
Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot
TL;DR: A DNN model, namely AdaNet, is introduced to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface, and results indicate that the system is capable of performing metric field inspection, and can serve as an effective tool for civil engineers.
71
Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM
Bing Li,Kenshin Ushiroda,Liang Yang,Qiang Song,Jizhong Xiao +4 more
- 31 Jul 2017
TL;DR: A novel climbing robot, namely Rise-Rover, is presented to perform automated IE signal collection from concrete structures with IE signal analyzing based on machine learning techniques to automatically classify the IE signals.
54
Collaborative Mapping and Autonomous Parking for Multi-Story Parking Garage
TL;DR: This work presents a novel collaborative mapping and autonomous parking system for semi-structured multi-story parking garages, based on cooperative 3-D LiDAR point cloud registration and Bayesian probabilistic updating, and proposes a collaborative navigation approach for path planning when there are multiple vehicles in the parking garage.
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