Robot

Linguistics

Engineering

Philosophy

Marine engineering

Simultaneous localization and mapping

Extended Kalman filter

Archaeology

State vector

Economics

Computer science

Classical mechanics

Scanner

Geology

Oceanography

Laser

Physics

Point cloud

Mathematics

Optics

Laser scanning

Position (finance)

Geography

Feature (linguistics)

Mobile robot

Artificial intelligence

Kalman filter

Geometry

Underwater

Point (geometry)

Finance

Computer vision

Iterative closest point

Journal of Field Robotics

This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner