Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning

TL;DR

Voxblox introduces an efficient method to incrementally construct Euclidean Signed Distance Fields from Truncated Signed Distance Fields for real-time MAV planning, enabling better obstacle avoidance and environment understanding.

Contribution

The paper presents a novel real-time system that builds ESDFs from TSDFs more quickly and accurately than existing methods, suitable for onboard MAV navigation.

Findings

TSDFs can be built faster than Octomaps.

ESDFs derived from TSDFs are more accurate than from occupancy maps.

The system operates in real-time on a single CPU core onboard MAV.

Abstract

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.