Robotics
Signed distance field
Definition
A signed distance field represents a surface by assigning spatial locations a distance value whose sign distinguishes the two sides of the surface. Its zero level marks the surface, while the sign convention depends on the representation.
Also known as: SDF
Updated
Distance information helps plan clearance
A distance field lets a planner ask how far a candidate robot position lies from an obstacle. The Voxblox paper develops an incremental Euclidean signed distance field for onboard trajectory planning from sensor observations.
This supports trajectory optimization, where distance values and their spatial variation can guide a path away from nearby obstacles. The field remains a representation of the observed environment, not a guarantee about every unseen surface.
Euclidean and truncated fields differ
A Euclidean signed distance field represents distance to the nearest surface. A truncated signed distance field limits stored distances to a band and is often built from projective sensor measurements. Voxblox explains why a TSDF used for surface reconstruction is not directly interchangeable with an ESDF used for clearance queries.
Unknown space needs an explicit policy
The paper discusses how unobserved voxels are treated during mapping and planning. Absence of a surface measurement does not establish empty space. Before using a field for robot movement, check its sign convention, voxel resolution, truncation, and treatment of unknown regions. Here SDF means signed distance field, not the separate Simulation Description Format used by some simulators.
Sources
Related terms
Point cloud
A point cloud is a collection of points representing sampled locations in space, usually with three-dimensional coordinates. Individual points may also carry attributes such as color or return intensity.
Occupancy grid
An occupancy grid divides space into cells and records occupancy information for each cell. A two-dimensional robot map commonly distinguishes occupied, free, and unknown regions.
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.