Point cloud

Iterative closest point

Mobile robot

Mathematics

Economic growth

Noise (video)

Gene

Algorithm

Image (mathematics)

Statistics

Initialization

Ranging

Mathematical analysis

Measurement uncertainty

Calibration

Artificial intelligence

Economics

Matching (statistics)

Generalization

Covariance

Chemistry

Programming language

Transformation (genetics)

Geometry

Convergence (economics)

Sensor fusion

Computer science

Odometry

Point (geometry)

Robotics

Biochemistry

Telecommunications

Robot

IEEE Robotics and Automation Letters

In mobile robotics, scan matching of point clouds using Iterative Closest Point (ICP) allows estimating sensor displacements. It may prove important to assess the associated uncertainty about the obtained rigid transformation, especially for sensor fusion purposes. In this paper we propose a novel approach to 3D uncertainty of ICP that accounts for all the sources of error as listed in Censi's pioneering work [1], namely wrong convergence, underconstrained situations, and sensor noise. Our approach builds on two facts. First, the uncertainty about the ICP's output fully depends on the initialization accuracy. Thus speaking of the covariance of ICP makes sense only in relation to the initialization uncertainty, which generally stems from odometry errors. We capture this using the unscented transform, which also reflects correlations between initial and final uncertainties. Then, assuming white sensor noise leads to overoptimism as ICP is biased owing to e.g. calibration biases, which we account for. Our solution is tested on publicly available real data ranging from structured to unstructured environments, where our algorithm predicts consistent results with actual uncertainty, and compares favorably to previous methods.

A New Approach to 3D ICP Covariance Estimation