A GPU mapping system for real-time robot motion planning

Abstract

We present in this paper an all-in-parallel mapping and perception framework for robotic navigation. When performing motion planning in cluttered environments, a local map is required to be updated real time. It is often necessary for a gradient-based trajectory optimization method to retrieve both occupancy information and distance to obstacles, in which occupancy grid maps (OGMs) and Euclidean signed distance fields (ESDFs) are often employed as a bridge between robotic perception and planning. To build the OGMs and ESDFs fast and efficiently, we propose a complete map construction system that is compatible with the commonly used on-board sensors. The proposed system constructs the OGMs and ESDFs in parallel on GPUs. The map increases with the movement of the vehicle by adapting a hashing data structure. The new data are fused continuously to the existing map by employing the parallel wavefront algorithm. Our approach can achieve real-time performance in limited on-board computing resources. It has been showed to outperform many existing real-time mapping systems that generate ESDFs on micro aerial vehicles.