Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic elements, determining when map features no longer correspond to the environment is essential for long-term utility. This work develops a feature-based model of the environment which evolves over time through feature persistence. Moreover, we augment the state-of-the-art sparse mapping model with a correlative structure that captures spatio-temporal properties, e.g. that nearby features frequently have similar persistence. We show that such relationships, typically addressed through an ad hoc formalism focusing only on feature repeatability, are crucial to evaluate through a probabilistically principled approach. The joint posterior over feature persistence can be computed efficiently and used to improve online data association decisions for localization. The proposed algorithms are validated in numerical simulation and using publicly available data sets.

This paper reports on a model-assisted bundle adjustment framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectation-maximization (EM) class of algorithms, we introduce a hidden binary label for each visual feature that indicates if that feature is considered part of the nominal model, or if the feature corresponds to 3D structure that is absent from this model. Therefore, in addition to improved estimates of the feature locations, we can also label the features based on their deviation from the model. We show that this method is a special case of the Gaussian max-mixtures framework, which can be efficiently incorporated into state-of-the-art graph-based simultaneous localization and mapping (SLAM) solvers. We provide field tests taken from the Bluefin Robotics Hovering Autonomous Underwater Vehicle (HAUV) surveying the SS Curtiss.

Feature learning has become popular in robotics due to recent advances in machine learning. In this paper, we propose a novel method to utilize the high-dimensional features from these techniques as observations in Bayesian estimation problems in a real-time manner. We develop an approach that: 1) pre-processes the observations and maps them into a new space with both reduced dimensions and a linear relationship to the estimation states; and 2) estimates the uncertainty of resulting outputs using data perturbation. The result is that deep learning approaches can be combined with more traditional filtering approaches like the Kalman filter (KF) to achieve state-of-the-art real-time performance. We validate the method by presenting the first real-time application of underwater robot localization using an imaging sonar. The proposed technique shows similar localization accuracy to benchmark approaches while simultaneously achieving real-time performance.

This paper reports on a 3D photomosaicing pipeline using data collected from an autonomous underwater vehicle performing simultaneous localization and mapping (SLAM). The pipeline projects and blends 2D imaging sonar data onto a large-scale 3D mesh that is either given a priori or derived from SLAM. Compared to other methods that generate a 2D-only mosaic, our approach produces 3D models that are more structurally representative of the environment being surveyed. Additionally, our system leverages recent work in underwater SLAM using sparse point clouds derived from Doppler velocity log range returns to relax the need for a prior model. We show that the method produces reasonably accurate surface reconstruction and blending consistency, with and without the use of a prior mesh. We experimentally evaluate our approach with a Hovering Autonomous Underwater Vehicle (HAUV) performing inspection of a large underwater ship hull.

This paper reports on a method for an autonomous underwater vehicle to perform real-time visual simultaneous localization and mapping (SLAM) on large ship hulls over multiple sessions. Along with a monocular camera, our method uses a piecewise-planar model to explicitly optimize the ship hull surface in our factor-graph framework, and anchor nodes to co-register multiple surveys. To enable realtime performance for long-term SLAM, we use the recent Generic Linear Constraints (GLC) framework to sparsify our factor-graph. This paper analyzes how our single-session SLAM techniques can be used in the GLC framework, and describes a particle filter reacquisition algorithm so that an underwater session can be automatically re-localized to a previously built SLAM graph. We provide real-world experimental results involving automated ship hull inspection, and show that our localization filter out-performs Fast Appearance-Based Mapping (FAB-MAP), a popular place-recognition system. Using our approach, we can automatically align surveys that were taken days, months, and even years apart.

This paper reports on methods for incorporating camera calibration uncertainty into a two-view sparse bundle adjustment (SBA) framework. The co-registration of two images is useful in mobile robotics for determining motion over time. These camera measurements can constrain a robot's relative poses so that the trajectory and map can be estimated in a technique known as simultaneous localization and mapping (SLAM). Here, we comment on the importance of propagating uncertainty in both feature extraction and camera calibration in visual pose-graph SLAM. We derive an improved pose covariance estimate that leverages the Unscented Transform, and compare its performance to previous methods in both simulated and experimental trials. The two experiments reported here involve data from a camera mounted on a KUKA robotic arm (where a precise ground-truth trajectory is available) and a Hovering Autonomous Underwater Vehicle (HAUV) for large-scale autonomous ship hull inspection.

This paper reports on the use of planar patches as features in a real-time simultaneous localization and mapping (SLAM) system to model smooth surfaces as piecewise-planar. This approach works well for using observed point clouds to correct odometry error, even when the point cloud is sparse. Such sparse point clouds are easily derived by Doppler velocity log sensors for underwater navigation. Each planar patch contained in this point cloud can be constrained in a factor-graph-based approach to SLAM so that neighboring patches are sufficiently coplanar so as to constrain the robot trajectory, but not so much so that the curvature of the surface is lost in the representation. To validate our approach, we simulated a virtual 6-degree of freedom robot performing a spiral-like survey of a sphere, and provide real-world experimental results for an autonomous underwater vehicle used for automated ship hull inspection. We demonstrate that using the sparse 3D point cloud greatly improves the self-consistency of the map. Furthermore, the use of our piecewise-planar framework provides an additional constraint to multi-session underwater SLAM, improving performance over monocular camera measurements alone.