Underwater inspection, maintenance and repair (IMR) operations are being increasingly robotized in order to reduce safety issues and costs. These robotic systems rely on vision sensors to perform fundamental tasks, such as navigation and object recognition and manipulation. Especially, active optical 3D scanners are commonly used due to the domain-specific challenges of underwater imaging. This paper presents an exhaustive survey on the state of the art of optical 3D underwater scanners. A literature review on light projection and light-sensing technologies is presented. Moreover, quantitative performance comparisons of underwater 3D scanners present in the literature and commercial products are carried out.

Underwater 3-D laser scanners are an essential type of sensors used by unmanned underwater vehicle (UUVs) for operations such as inspection, navigation, and object recognition and manipulation. This article presents a novel 3-D laser scanner, which uses a 2-axis mirror to project straight lines into the water by compensating for refraction-related distortions. This is achieved by projecting optimally curved lines, so that the refraction when they enter the water transforms them into straight lines. The relevance of this approach lies in the fact that 3-D triangulation using planes is noticeably faster than using elliptic cones. The goal of this work is twofold: first, to prove that refraction-related distortions can in practice be compensated for by using a 2-axis mirror, and second, to present a simple calibration algorithm that only needs to compute the coefficients of polynomial functions. To the best of the authors’ knowledge, the prototype presented in this article is the first laser line scanner that actively counteracts the refraction of the projected light in the context of underwater robotics.

Underwater 3D laser scanners are an essential type of sensor used by unmanned underwater vehicles (UUVs) for operations such as navigation, inspection and object recognition and manipulation. These sensors need to be able to provide highly accurate 3D data at fast refresh rates in order to accomplish these tasks. Usually, these scanners rely on a rotating mirror actuated by a galvanometer. However, the light planes steered by this type of mirrors are typically deformed into cones due to refraction. In order to produce accurate results, this distortion needs to be taken into account, which increases the computational cost of the 3D reconstruction. A novel approach consisting in using a biaxial MEMS mirror is proposed in this paper. The second rotational degree of freedom of the mirror can be used to project optimally curved light shapes, so that the refraction process transforms them into planes. Being able to model the light surfaces as planes rather than cones can significantly reduce the computation time of the 3D reconstruction. In order to do so, an exhaustive model of the complete light trajectories is presented. To the best of the authors' knowledge, this paper constitutes the first attempt to model and counteract the distortion in the scanning pattern introduced by a biaxial mirror and a double refraction process in the context of underwater robotics.

Underwater 3D laser scanners are an essential type of sensor used by unmanned underwater vehicles (UUVs) for operations such as navigation, inspection, and object recognition and manipulation. Scanners that acquire 3D data by sweeping a laser plane across the scene can provide very high lateral resolution. However, their data may suffer from rolling shutter effect if the change of pose of the robot with respect to the scanned target during the sweep is not negligible. In order to compensate for motion-related distortions without the need for point cloud postprocessing, the 6-DoF pose at which the scanner acquires each line needs to be accurately known. In the underwater domain, autonomous vehicles are often equipped with a high-end inertial navigation system (INS) that provides reliable navigation data. Nonetheless, the relative pose of the 3D scanner with respect to the inertial reference frame of the robot is not usually known a priori. Therefore, this paper uses an ego-motion-based calibration algorithm to calibrate the extrinsic parameters of the visual-inertial sensor pair. Simulations are performed to quantify how miscalibration affects motion-related distortion. The method is also evaluated experimentally in laboratory conditions.

Robots are usually equipped with 3D range sensors such as laser line scanners (LLSs) or lidars. These sensors acquire a full 3D scan in a line by line manner while the robot is in motion. All the lines can be referred to a common coordinate frame using data from inertial sensors. However, errors from noisy inertial measurements and inaccuracies in the extrinsic parameters between the scanner and the robot frame are also projected onto the shared frame. This causes a deformation in the final scan containing all the lines, which is known as motion distortion . Rigid point cloud registration with methods like ICP is therefore not well suited for such distorted scans. In this paper we present a non-rigid registration method that finds the rigid transformation to be applied to each line in the scan in order to match an existing model. We fully leverage the continuous and relatively smooth robot motion with respect to the scanning time to formulate our method reducing the computational complexity while improving accuracy. We use synthetic and real data to benchmark our method against a state-of-the-art non-rigid registration method. Finally, the source code for the algorithm is made publicly available. ¹