Voxblox：機上計画のためのインクリメンタル3Dユークリッド符号付距離場
Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning
TSDFからESDFをインクリメンタルに構築する手法を提案し、単一CPUコアでリアルタイム動作するMAV搭載の軌道最適化計画を実現した。
著者: Helen Oleynikova, Zachary Taylor, Marius Fehr, Juan Nieto, Roland Siegwart
分類: cs.RO
原文アブストラクト
Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored environments require fast and flexible local planning, which can replan when new parts of the map are explored. Trajectory optimization methods fulfill these needs, but require obstacle distance information, which can be given by Euclidean Signed Distance Fields (ESDFs). We propose a method to incrementally build ESDFs from Truncated Signed Distance Fields (TSDFs), a common implicit surface representation used in computer graphics and vision. TSDFs are fast to build and smooth out sensor noise over many observations, and are designed to produce surface meshes. Meshes allow human operators to get a better assessment of the robot's environment, and set high-level mission goals. We show that we can build TSDFs faster than Octomaps, and that it is more accurate to build ESDFs out of TSDFs than occupancy maps. Our complete system, called voxblox, will be available as open source and runs in real-time on a single CPU core. We validate our approach on-board an MAV, by using our system with a trajectory optimization local planner, entirely on-board and in real-time.
