In this work we present a real-time approach for local trajectory replanning for MAVs. Current trajectory generation methods for multicopters achieve high success rates in cluttered environments, but assume the environment is static and require prior knowledge of the map. In our work we utilize the results of such planners and extend them with local replanning algorithm that can handle unmodeled (possibly dynamic) obstacles while keeping MAV close to the global trajectory. To make our approach real-time capable we maintain information about the environment around MAV in an occupancy grid stored in 3D circular buffer that moves together with a drone, and represent the trajectories using uniform B-splines. This representation ensures that trajectory is sufficiently smooth and at the same time allows efficient optimization.
Another popular approach to environment mapping is voxel hashing, which was proposed by and used in @cite_21 . It is mainly used for storing a truncated signed distance function representation of the environment. In this case, only a narrow band of measurements around the surface is inserted and only the memory required for that sub-volume is allocated. However, when full measurements must be inserted or the dense information must be stored the advantages of this approach compared to those of the other approaches are not significant.
@cite_21
2561394090
Truncated Signed Distance Fields (TSDFs) have become a popular tool in 3D reconstruction, as they allow building very high-resolution models of the environment in real-time on GPU. However, they have rarely been used for planning on robotic platforms, mostly due to high computational and memory requirements. We propose to reduce these requirements by using large voxel sizes, and extend the standard TSDF representation to be faster and better model the environment at these scales. We also propose a method to build Euclidean Signed Distance Fields (ESDFs), which are a common representation for planning, incrementally out of our TSDF representation. ESDFs provide Euclidean distance to the nearest obstacle at any point in the map, and also provide collision gradient information for use with optimization-based planners. We validate the reconstruction accuracy and real-time performance of our combined system on both new and standard datasets from stereo and RGB-D imagery. The complete system will be made available as an open-source library called voxblox.