20 accepted papers
Water caustics are commonly observed in seafloor imaging data from shallow-water areas. Traditional methods that remove caustic patterns from images often rely on 2D filtering or pre-training on an annotated dataset, hindering the performance when generalizing to real-world seafloor data with 3D str
Visual topic modeling (VTM) provides key insight into data sets based on learned semantic topic models. The Gaussian-Dirichlet Random Field (GDRF), a state-of-the-art VTM technique, models these semantic topics in continuous space as densities. However, ambiguity in learned topics is a disadvantage…
With the goal of developing low-cost and innovative perception and localization techniques for autonomous vehicles, this work explores a system that solely relies on a LoRa receiver and a magnetometer for agent localization within urban environments. Using the received signal strength from LoRa beac…
Fiducial markers are important tools for robotic navigation and imaging, enabling accurate localization and tracking of objects in challenging environments. In this paper, we present AcTag, a new fiducial marker design for use underwater with imaging sonar and cameras, as well as a method for the de…
Continuous-time estimation using splines on Lie groups has been gaining traction in the literature due to the ability to incorporate high-frequency sensor data without introducing new optimization parameters. However, evaluating time derivatives and Jacobians of Lie group splines is computationally
This paper presents a method for the robust selection of measurements in a simultaneous localization and mapping (SLAM) framework. Existing methods check consistency or compatibility on a pairwise basis, however many measurement types are not sufficiently constrained in a pairwise scenario to determ…
Due to the difficulty and expense of underwater field trials, a high fidelity underwater simulator is a necessity for testing and developing algorithms. To fill this need, we present HoloOcean, an open source underwater simulator, built upon Unreal Engine 4 (UE4). HoloOcean comes equipped with multi…
Sonar sensors play an integral part in underwater robotic perception by providing imagery at long distances where standard optical cameras cannot. They have proven to be an important part in various robotic algorithms including localization, mapping, and structure from motion. Unfortunately, generat…
In this work, we investigate the problem of incre-mentally solving constrained non-linear optimization problems formulated as factor graphs. Prior incremental solvers were either restricted to the unconstrained case or required periodic batch relinearizations of the objective and constraints which a…
Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However,…
We address the problem of class agnostic, joint instance segmentation of scene data. While learning-based semantic instance segmentation methods have achieved impressive progress, their use is limited in robotics applications due to reliance on expensive training data annotations and assumptions of…
Recent advances in the utilization of Lie Groups for robotic localization have led to dramatic increases in the accuracy of estimation and uncertainty characterization. One of the novel methods, the Invariant Extended Kalman Filter (InEKF) extends the Extended Kalman Filter (EKF) by leveraging the f
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and po…
In this paper we present a novel multi-stereo visual-inertial odometry (VIO) framework which aims to improve the robustness of a robot's state estimate during aggressive motion and in visually challenging environments. Our system uses a fixed-lag smoother which jointly optimizes for poses and landma…
In this paper, we introduce an ambiguity-aware robust active SLAM (ARAS) framework that makes use of multi-hypothesis state and map estimations to achieve better robustness. Ambiguous measurements can result in multiple probable solutions in a multi-hypothesis SLAM (MH-SLAM) system if they are tempo…
In this paper, we present an active SLAM framework for volumetric exploration of 3D underwater environments with multibeam sonar. Recent work in integrated SLAM and planning performs localization while maintaining volumetric free-space information. However, an absence of informative loop closures ca…
Fast, aerial navigation in cluttered environments requires a suitable map representation for path planning. In this paper, we propose the use of an efficient, structured multiresolution representation that expands the sensor range of dense local grids for memory-constrained platforms. While similar…
A robot operating in the world constantly receives information about its environment in the form of new measurements at every time step. Smoothing-based estimation methods seek to optimize for the most likely robot state estimate using all measurements up till the current time step. Existing methods…
Autonomous navigation requires an accurate model or map of the environment. While dramatic progress in the prior two decades has enabled large-scale simultaneous localization and mapping (SLAM), the majority of existing methods rely on non-linear optimization techniques to find the maximum likelihoo…
This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous localization and mapping (SLAM). Existing robust SLAM methods assume a good initialization or an “odometry backbone” to classify inlier and outlier loop closures. In the multi-robot case, these…