Details

Autor(en) / Beteiligte

• Oleynikova, Helen
• Taylor, Zachary
• Fehr, Marius
• Siegwart, Roland
• Nieto, Juan

Titel

Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning

Ist Teil von

• 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, p.1366-1373

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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. 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, is 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.