Euclidean Signed Distance Field (ESDF) is useful for online motion planning of aerial robots since it can easily query the distance and gradient information against obstacles. Fast incrementally built ESDF map is the bottleneck for conducting real-time motion planning. In this paper, we investigate this problem and propose a mapping system called FIESTA to build global ESDF map incrementally. By introducing two independent updating queues for inserting and deleting obstacles separately, and using Indexing Data Structures and Doubly Linked Lists for map maintenance, our algorithm updates as few as possible nodes using a BFS framework. Our ESDF map has high computational performance and produces nearoptimal results. We show our method outperforms other upto-date methods in term of performance and accuracy by both theory and experiments. We integrate FIESTA into a completed quadrotor system and validate it by both simulation and onboard experiments. We release our method as opensource software for the community 1 .
Motion planning is a vital module for unmanned aerial vehicles (UAVs), especially in scenarios of autonomous navigation and operation. This survey delivers some recent state‐of‐the‐art UAV motion planning algorithms and related applications. The logic flow of this survey is divided as the path finding, which is the front‐end of most motion planning systems, and the trajectory optimisation, which usually serves as the back‐end. Motivation, methodology, problem formulation and derivation are given in this survey, in detail. Finally, a section about real‐world applications is given, where roles and effectiveness of most popular motion planning methods are revealed.
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