FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots 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 realtime 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 uptodate 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 11https:github.comhlx1996FIESTA

Realtime Trajectory Generation for Quadrotors using Bspline based Nonuniform Kinodynamic Search In this paper, we propose a timeefficient approach to generate safe, smooth and dynamically feasible trajectories for quadrotors in the obstaclecluttered environment. By using the uniform Bspline to represent trajectories, we transform the trajectory planning to a graphsearch problem of Bspline control points in discretized space. Highly strict convex hull property of Bspline is derived to guarantee the dynamical feasibility of the entire trajectory. A novel nonuniform kinodynamic search strategy is adopted, and the step length is dynamically adjusted during the search process according to the Euclidean signed distance field (ESDF), making the trajectory achieve reasonable timeallocation and be away from obstacles. Nonstatic initial and goal states are allowed, therefore it can be used for online local replanning as well as global planning. Extensive simulation and hardware experiments show that our method achieves higher performance compared with the stateoftheart method.