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David M. Rosen

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13 papers

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UAI Conference 2025 Conference Paper

A Trust-Region Method for Graphical Stein Variational Inference

Liam Pavlovic
David M. Rosen

Stein variational inference (SVI) is a sample-based approximate Bayesian inference technique that generates a sample set by jointly optimizing the samples’ locations to minimize an information-theoretic measure of discrepancy with the target probability distribution. SVI thus provides a fast and significantly more sample-efficient approach to Bayesian inference than traditional (random-sampling-based) alternatives. However, the optimization techniques employed in existing SVI methods struggle to address problems in which the target distribution is high-dimensional, poorly-conditioned, or non-convex, which severely limits the range of their practical applicability. In this paper, we propose a novel trust-region optimization approach for SVI that successfully addresses each of these challenges. Our method builds upon prior work in SVI by leveraging conditional independences in the target distribution (to achieve high-dimensional scaling) and second-order information (to address poor conditioning), while additionally providing an effective adaptive step control procedure, which is essential for ensuring convergence on challenging non-convex optimization problems. Experimental results show our method achieves superior numerical performance, both in convergence rate and sample accuracy, and scales better in high-dimensional distributions, than previous SVI techniques.

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ICRA Conference 2025 Conference Paper

Distributed Certifiably Correct Range-Aided SLAM

Alexander Thoms
Alan Papalia
Jared Velasquez
David M. Rosen
Sriram Narasimhan

Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance.

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ICRA Conference 2024 Conference Paper

OASIS: Optimal Arrangements for Sensing in SLAM

Pushyami Kaveti
Matthew Giamou
Hanumant Singh
David M. Rosen

The number and arrangement of sensors on mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates.

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ICRA Conference 2023 Conference Paper

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

Alan Papalia
Joseph Morales
Kevin J. Doherty 0001
David M. Rosen
John J. Leonard

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.

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IROS Conference 2022 Conference Paper

Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Yulun Tian
Amrit Singh Bedi
Alec Koppel
Miguel Calvo-Fullana
David M. Rosen
Jonathan P. How

We present the first distributed optimization al-gorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localization and mapping (SLAM) and structure-from-motion (SfM) applications. Our method allows agents to cooperatively reconstruct a shared geometric model on a central server by fusing individual observations, but without the need to transmit potentially sensitive information about the agents themselves (such as their locations). Furthermore, to alleviate the burden of communication during iterative optimization, we design a set of communication triggering conditions that enable agents to selectively upload a targeted subset of local information that is useful to global optimization. Our approach thus achieves significant communication reduction with minimal impact on optimization performance. As our main theoretical contribution, we prove that our method converges to first-order critical points with a global sublinear convergence rate. Numerical evaluations on bundle adjustment problems from collaborative SLAM and SfM datasets show that our method performs competitively against existing distributed techniques, while achieving up to 78% total communication reduction.

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ICRA Conference 2022 Conference Paper

Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM

Kevin J. Doherty 0001
David M. Rosen
John J. Leonard

In this work we present the first initialization methods equipped with explicit performance guarantees that are adapted to the pose-graph simultaneous localization and mapping (SLAM) and rotation averaging (RA) problems. SLAM and rotation averaging are typically formalized as large-scale nonconvex point estimation problems, with many bad local minima that can entrap the smooth optimization methods typically applied to solve them; the performance of standard SLAM and RA algorithms thus crucially depends upon the quality of the estimates used to initialize this local search. While many initialization methods for SLAM and RA have appeared in the literature, these are typically obtained as purely heuristic approximations, making it difficult to determine whether (or under what circumstances) these techniques can be reliably deployed. In contrast, in this work we study the problem of initialization through the lens of spectral relaxation. Specifically, we derive a simple spectral relaxation of SLAM and RA, the form of which enables us to exploit classical linear-algebraic techniques (eigenvector perturbation bounds) to control the distance from our spectral estimate to both the (unknown) ground-truth and the global minimizer of the estimation prob lem as a function of measurement noise. Our results reveal the critical role that spectral graph-theoretic properties of the measurement network play in controlling estimation accuracy; moreover, as a by-product of our analysis we obtain new bounds on the estimation error for the maximum likelihood estimators in SLAM and RA, which are likely to be of independent interest. Finally, we show experimentally that our spectral estimator is very effective in practice, producing initializations of comparable or superior quality at lower computational cost compared to existing state-of-the-art techniques.

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IROS Conference 2022 Conference Paper

Spectral Measurement Sparsification for Pose-Graph SLAM

Kevin J. Doherty 0001
David M. Rosen
John J. Leonard

Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of “lifelong” SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbounded during long-term navigation. To address this, we propose a spectral approach for pose graph sparsification which maximizes the algebraic connectivity of the sparsified measurement graphs, a key quantity which has been shown to control the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for maximizing algebraic connectivity), which is based on convex relaxation, is simple and computationally inexpensive, and admits formal post hoc performance guarantees on the quality of the solutions it provides. In experiments on benchmark pose-graph SLAM datasets, we show that our approach quickly produces high-quality sparsification results which retain the connectivity of the graph and, in turn, the quality of corresponding SLAM solutions, as compared to a baseline approach which does not consider graph connectivity.

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ICRA Conference 2016 Conference Paper

Towards lifelong feature-based mapping in semi-static environments

David M. Rosen
Julian Mason
John J. Leonard

The feature-based graphical approach to robotic mapping provides a representationally rich and computationally efficient framework for an autonomous agent to learn a model of its environment. However, this formulation does not naturally support long-term autonomy because it lacks a notion of environmental change; in reality, “everything changes and nothing stands still, ” and any mapping and localization system that aims to support truly persistent autonomy must be similarly adaptive. To that end, in this paper we propose a novel feature-based model of environmental evolution over time. Our approach is based upon the development of an expressive probabilistic generative feature persistence model that describes the survival of abstract semi-static environmental features over time. We show that this model admits a recursive Bayesian estimator, the persistence filter, that provides an exact online method for computing, at each moment in time, an explicit Bayesian belief over the persistence of each feature in the environment. By incorporating this feature persistence estimation into current state-of-the-art graphical mapping techniques, we obtain a flexible, computationally efficient, and information-theoretically rigorous framework for lifelong environmental modeling in an ever-changing world.

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ICRA Conference 2015 Conference Paper

A convex relaxation for approximate global optimization in simultaneous localization and mapping

David M. Rosen
Charles DuHadway
John J. Leonard

Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a high-dimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance of any such approach depends crucially upon initializing the optimization algorithm near a good solution for the inference problem, a condition that is often difficult or impossible to guarantee in practice. To address this limitation, in this paper we present a formulation of the SLAM M-estimation with the property that, by expanding the feasible set of the estimation program, we obtain a convex relaxation whose solution approximates the globally optimal solution of the SLAM inference problem and can be recovered using a smooth optimization method initialized at any feasible point. Our formulation thus provides a means to obtain a high-quality solution to the SLAM problem without requiring high-quality initialization.

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IROS Conference 2015 Conference Paper

Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions

Luca Carlone
David M. Rosen
Giuseppe Carlo Calafiore
John J. Leonard
Frank Dellaert

State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on the quality of the estimate. This paper shows that Lagrangian duality is a powerful tool to assess the quality of a given candidate solution. Our contribution is threefold. First, we discuss a revised formulation of the SLAM inference problem. We show that this formulation is probabilistically grounded and has the advantage of leading to an optimization problem with quadratic objective. The second contribution is the derivation of the corresponding Lagrangian dual problem. The SLAM dual problem is a (convex) semidefinite program, which can be solved reliably and globally by off-the-shelf solvers. The third contribution is to discuss the relation between the original SLAM problem and its dual. We show that from the dual problem, one can evaluate the quality (i. e. , the suboptimality gap) of a candidate SLAM solution, and ultimately provide a certificate of optimality. Moreover, when the duality gap is zero, one can compute a guaranteed optimal SLAM solution from the dual problem, circumventing non-convex optimization. We present extensive (real and simulated) experiments supporting our claims and discuss practical relevance and open problems.

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ICRA Conference 2014 Conference Paper

Inference over heterogeneous finite-/infinite-dimensional systems using factor graphs and Gaussian processes

David M. Rosen
Guoquan Huang 0001
John J. Leonard

The ability to reason over partially observable networks of interacting states is a fundamental competency in probabilistic robotics. While the well-known factor graph and Gaussian process models provide flexible and computationally efficient solutions for this inference problem in the special cases in which all of the hidden states are either finite-dimensional parameters or real-valued functions, respectively, in many cases we are interested in reasoning about heterogeneous networks whose hidden states are comprised of both finite-dimensional parameters and functions. To that end, in this paper we propose a novel probabilistic generative model that incorporates both factor graphs and Gaussian processes to model these heterogeneous systems. Our model improves upon prior approaches to inference within these networks by removing the assumption of any specific set of conditional independences amongst the modeled states, thereby significantly expanding the class of systems that can be represented. Furthermore, we show that inference within this model can always be performed by means of a two-stage procedure involving inference within a factor graph followed by inference over a Gaussian process; by exploiting fast inference methods for the individual factor graph and Gaussian process models to solve each of these subproblems in succession, we thus obtain a general framework for computationally efficient inference over heterogeneous finite-/infinite-dimensional systems.

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ICRA Conference 2013 Conference Paper

Robust incremental online inference over sparse factor graphs: Beyond the Gaussian case

David M. Rosen
Michael Kaess
John J. Leonard

Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e. g. incremental smoothing and mapping (iSAM) or Robust Incremental least-Squares Estimation (RISE)) for performing online incremental maximum likelihood estimation over these models, they generally require that the distribution of interest factors as a product of Gaussians, a rather restrictive assumption. In this paper, we investigate the possibility of performing efficient incremental online estimation over sparse factor graphs in the non-Gaussian case. Our main result is a method that generalizes iSAM and RISE by removing the assumption of Gaussian factors, thereby significantly expanding the class of distributions to which these algorithms can be applied. The generalization is achieved by means of a simple algebraic reduction that under relatively mild conditions (boundedness of each of the factors in the distribution of interest) enables an instance of the general maximum likelihood estimation problem to be reduced to an equivalent instance of least-squares minimization that can be solved efficiently online by application of iSAM or RISE. Through this construction we obtain robust, computationally efficient, and mathematically correct incremental online maximum likelihood estimators for non-Gaussian distributions over sparse factor graphs.

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ICRA Conference 2012 Conference Paper

An incremental trust-region method for Robust online sparse least-squares estimation

David M. Rosen
Michael Kaess
John J. Leonard

Many online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals. Under these conditions, maximum likelihood estimation corresponds to solving a sequence of sparse least-squares minimization problems in which additional summands are added to the objective function over time. In this paper we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg trust-region method suitable for use in online sparse least-squares minimization. As a trust-region method, Powell's Dog-Leg enjoys excellent global convergence properties, and is known to be considerably faster than both Gauss-Newton and Levenberg-Marquardt when applied to sparse least-squares problems. Consequently, RISE maintains the speed of current state-of-the-art incremental sparse least-squares methods while providing superior robustness to objective function nonlinearities.

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