Abstract
Mapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping framework. In this framework, we divided the map into three layers: awareness, local, and global. The awareness map is constructed in cylindrical coordinate, enabling fast raycasting. The local map is a probability-based volumetric map. The global map adopts dynamic memory management, allocating memory for the active mapping area, and recycling the memory from the inactive mapping area. We implemented this mapping framework in three parallel threads: awareness thread, local-global thread, and visualization thread. Finally, we evaluated the mapping kit in both the simulation environment and the real-world scenario with the vision-based sensors. The framework supports different kinds of map outputs for the global or local path planners. The implementation is open-source for the research community.
Highlights
The autonomous UAV navigation system senses the environment and reacts to it so that the UAV can move from one place to another safely even in an unknown environment
The mapping kit aims to bridge the gap between the localization and path planning modules
Lau et al [20] divided the entire mapping plane with the Voronoi diagram and carried out an incremental update of the map. Another kind of SDFs is the truncated signed distance fields (TSDFs), in which the distance represents the distance from the occupied surface to the center of the sensor along the ray direction
Summary
The autonomous UAV navigation system senses the environment and reacts to it so that the UAV can move from one place to another safely even in an unknown environment. The mapping kit aims to bridge the gap between the localization and path planning modules. If every voxel stores 64 bytes of information, the memory needed for such a map is merely 100 MB. The dynamic memory management will be applied to the sub-map instead of every single voxels. In this novel mapping framework (Figure 1), voxels are stored in an organized data structure. The framework takes two measurements to reduce the uncertainties and increase the map’s robustness with the raycasting-based visibility check and probability-based occupancy state. Adopted different measurements to increase the robustness and feasibility of the mapping kit These measurements include efficient raycasting-based visibility check, probability-based representation of the occupancy state and dynamic memory management.
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