3 citations · 5 across the 3 of their papers we have counts for
4 papers
Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration
Yuanfan Xu, Jincheng Yu, Jiahao Tang +5
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Never…
Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment
Hongyu Song, Jincheng Yu, Jiantao Qiu +5
For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration…
Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
Feng Gao, Jincheng Yu, Hao Shen +2
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of v…
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks
Wenshuo Li, Jincheng Yu, Xuefei Ning +4
Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN), develops rapidly and is applied to many tasks, such as image classification, face recognition, image se…