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Improved lazy theta algorithm based on octree map for path planning of UAV

Defence Technology（防务技术） 2023年第5期23卷 8-18页

作者： Meng-shun Yuan Tong-le Zhou Mou Chen College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing211106China

This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

关键词： Unmanned aerial vehicle Path planning Lazy theta*algorithm octree map Line-of-sight algorithm

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3D Semantic mapping Based on Convolutional Neural Networks

3D Semantic Mapping Based on Convolutional Neural Networks

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第37届中国控制会议

作者： Jing Li Yanyu Liu Junzheng Wang Min Yan Yanzhi Yao Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation,localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks（CNNs） and Simultaneous Localization and mapping（SLAM） to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. The semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.

关键词： CNNs SLAM Semantic map octree map

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