Many problems in robotics, such asestimating the state from noisy sensor data or aligning two point clouds, can be posed and solved as least-squares problems. Unfortunately, vanilla nonminimal solvers for least-squares problems are notoriously sensitive to outliers and initialization errors. The conventional approach to outlier rejection is to use a robust loss function, which is typically selected and tuned a priori. A newly developed approach to handle large initialization errors is graduated nonconvexity (GNC), which is defined for a particular choice of a robust loss function. The main contribution of this article is to combine these two approaches by using an adaptive kernel within a GNC optimization scheme. This brings a solution to least-squares problems that is robust to both outliers and initialization errors, without the need for model selection and tuning. Simulations and experiments demonstrate that the proposed method is more robust compared to non-GNC counterparts and performs on par with other GNC-tailored loss functions.

The recent development of high-precision subsea optical scanners allows for 3D keypoint detectors and feature descriptors to be leveraged on point cloud scans from subsea environments. However, the literature lacks a comprehensive survey to identify the best combination of detectors and descriptors to be used in these challenging and novel environments. This paper aims to identify the best detector/descriptor pair using a challenging field dataset collected using a commercial underwater laser scanner. Furthermore, studies have shown that incorporating texture information to extend geometric features adds robustness to feature matching on synthetic datasets. This paper also proposes a novel method of fusing images with underwater laser scans to produce coloured point clouds, which are used to study the effectiveness of 6D point cloud descriptors.

Accurate, self-consistent bathymetric maps are needed to monitor changes in subsea environments and infrastructure. These maps are increasingly collected by underwater vehicles, and mapping requires an accurate vehicle navigation solution. Commercial off-the-shelf (COTS) navigation solutions for underwater vehicles often rely on external acoustic sensors for localization; however, survey-grade acoustic sensors are expensive to deploy and limit the range of the vehicle. Techniques from the field of simultaneous localization and mapping, particularly loop closures, can improve the quality of the navigation solution over dead reckoning, but are difficult to integrate into COTS navigation systems. This article presents a method to improve the self-consistency of bathymetric maps by smoothly integrating loop-closure measurements into the state estimate produced by a commercial subsea navigation system. Integration is done using a white-noise-on-acceleration motion prior, without access to raw sensor measurements or proprietary models. Improvements in map self-consistency are shown for both simulated and experimental datasets, including a 3-D scan of an underwater shipwreck in Wiarton, ON, Canada.

Point cloud registration is a means of achieving loop closure correction within a simultaneous localization and mapping (SLAM) algorithm. Data association is a critical component in point cloud registration, and can be very challenging in feature-depleted environments such as seabed. This paper presents a point cloud registration pipeline for performing loop closure correction in feature-depleted subsea environments using data collected from an optical scanner. The pipeline uses Gaussian process regression to extract keypoint sets, and a weighted network alignment algorithm to propose point correspondences. A variant of the iterative closest point (ICP) registration algorithm is used to perform fine alignment, with point correspondences informed by the mappings determined following the network alignment step. The developed registration pipeline is deployed with success on a challenging section of field data containing topography that cannot be resolved using conventional imaging sonar.