Adaptive-Resolution Gaussian Process Mapping for Efficient UAV-based Terrain Monitoring

Unmanned aerial vehicles (UAVs) are rapidly gaining popularity in a variety of environmental monitoring tasks. A key requirement for autonomous operation is the ability to perform efficient environmental mapping and path planning online, given their limited on-board resources constraining operation time and computational capacity. To address this, we present an adaptive-resolution approach for terrain mapping based on the Nd-tree structure and Gaussian Processes (GPs). Our approach enables retaining details in areas of interest using higher map resolutions while compressing information in uninteresting areas at coarser resolutions to achieve a compact map representation of the environment. A key aspect of our approach is an integral kernel encoding spatial correlation of 2D grid cells, which enables merging uninteresting grid cells in a theoretically sound way. Results show that our approach is more efficient in terms of time and memory consumption without compromising on mapping quality. The resulting adaptive-resolution map accelerates the on-line adaptive path planning as well. Both performance enhancement in mapping and planning facilitate the efficiency of autonomous environmental monitoring with UAVs.